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2007 | Book

Hybrid Dynamic Systems in an Industry Design Application Read chapter Read first chapter

Soft Computing in Industrial Applications

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Editors: Ashraf Saad, Keshav Dahal, Muhammad Sarfraz, Rajkumar Roy

Publisher: Springer Berlin Heidelberg

Book Series : Advances in Intelligent and Soft Computing

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

Soft Computing admits approximate reasoning, imprecision, uncertainty and partial truth in order to mimic aspects of the remarkable human capability of making decisions in real-life and ambiguous environments. "Soft Computing in Industrial Applications" contains a collection of papers that were presented at the 11th On-line World Conference on Soft Computing in Industrial Applications, held in September-October 2006. This carefully edited book provides a comprehensive overview of the recent advances in the industrial applications of soft computing and covers a wide range of application areas, including data analysis and data mining, computer graphics, intelligent control, systems, pattern recognition, classifiers, as well as modeling optimization. The book is aimed at researchers and practitioners who are engaged in developing and applying intelligent systems principles to solving real-world problems. It is also suitable as wider reading for science and engineering postgraduate students.

Table of Contents

Frontmatter

Invited Keynote

Hybrid Dynamic Systems in an Industry Design Application
Abstract
The term hybrid dynamic system is a term for a mathematical system that combines behavior of a continuous nature with discontinuous changes. Such systems are often formed by the underlying computational representation of models used in the design of control and signal processing applications, for example in the automotive and aerospace industries. This paper outlines the benefits of Model-Based Design and illustrates how many different formalisms may be essential in model elaboration, such as time-based block diagrams, state transition diagrams, entity-flow networks, and multi-body diagrams. The basic elements of the underlying hybrid dynamic system computational representation are presented and it is shown how these elements combine to form different classes of behaviors that need to be handled for simulation.
Pieter J. Mosterman, Elisabeth M. O’Brien

Part I: Soft Computing in Computer Graphics, Imaging and Vision

Frontmatter
Object Recognition Using Particle Swarm Optimization on Fourier Descriptors
Abstract
This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outlines of the objects have been used for the whole process of the recognition. Fourier Descriptors have been used as features of the objects. From the analysis and results using Fourier Descriptors, the following questions arise: What is the optimum number of descriptors to be used? Are these descriptors of equal importance? To answer these questions, the problem of selecting the best descriptors has been formulated as an optimization problem. Particle Swarm Optimization technique has been mapped and used successfully to have an object recognition system using minimal number of Fourier Descriptors. The proposed method assigns, for each of these descriptors, a weighting factor that reflects the relative importance of that descriptor.
Muhammad Sarfraz, Ali Taleb Ali Al-Awami
Gestix: A Doctor-Computer Sterile Gesture Interface for Dynamic Environments
Abstract
In this paper, we design a sterile gesture interface for users, such as doctors/surgeons, to browse medical images in a dynamic medical environment. A vision-based gesture capture system interprets user’s gestures in real-time to navigate through and manipulate an image and data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. The gesture system relies on tracking of the user’s hand based on color-motion cues. A state machine switches from navigation gestures to others such as zoom and rotate. A prototype of the gesture interface was tested in an operating room by neurosurgeons conducting a live operation. Surgeon’s feedback was very positive.
Juan Wachs, Helman Stern, Yael Edan, Michael Gillam, Craig Feied, Mark Smith, Jon Handler
Differential Evolution for the Registration of Remotely Sensed Images
Abstract
This paper deals with the design and implementation of a software system based on Differential Evolution for the registration of images, and in its testing by means of a set of bidimensional remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A comparison is effected against a publicly available tool, showing the effectiveness of our method.
I. De Falco, A. Della Cioppa, D. Maisto, E. Tarantino
Geodesic Distance Based Fuzzy Clustering
Abstract
Clustering is a widely applied tool of data mining to detect the hidden structure of complex multivariate datasets. Hence, clustering solves two kinds of problems simultaneously, it partitions the datasets into cluster of objects that are similar to each other and describes the clusters by cluster prototypes to provide some information about the distribution of the data. In most of the cases these cluster prototypes describe the clusters as simple geometrical objects, like spheres, ellipsoids, lines, linear subspaces etc., and the cluster prototype defines a special distance function. Unfortunately in most of the cases the user does not have prior knowledge about the number of clusters and not even about the proper shape of prototypes. The real distribution of data is generally much more complex than these simple geometrical objects, and the number of clusters depends much more on how well the chosen cluster prototypes fit the distribution of data than on the real groups within the data. This is especially true when the clusters are used for local linear modeling purposes.
The aim of this paper is not to define a new distance norm based on a problem dependent cluster prototype but to show how the so called geodesic distance that is based on the exploration of the manifold the data lie on, can be used in the clustering instead of the classical Euclidean distance. The paper presents how this distance measure can be integrated within fuzzy clustering and some examples are presented to demonstrate the advantages of the proposed new methods.
Balazs Feil, Janos Abonyi

Part II: Control Systems

Frontmatter
Stability Analysis of the Simplest Takagi-Sugeno Fuzzy Control System Using Popov Criterion
Abstract
In our paper, the properties of the simplest Takagi-Sugeno (T-S) fuzzy controller are first investigated. Next, based on the well-known Popov criterion with graphical interpretation, a sufficient condition in the frequency domain is proposed to guarantee the globally asymptotical stability of the simplest T-S fuzzy control system. Since this sufficient condition is presented in the frequency do-main, it is of great significance in designing the simplest T-S fuzzy controller in the frequency domain.
Xiaojun Ban, X. Z. Gao, Xianlin Huang, Hang Yin
Identification of an Experimental Process by B-Spline Neural Network Using Improved Differential Evolution Training
Abstract
B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, differential evolution (DE) ( an evolutionary computation methodology ( can provide a stochastic search to adjust the control points of a BSNN are proposed. DE incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution and robustness. In this paper, we propose a modified DE using chaotic sequence based on logistic map to train a BSNN. The numerical results presented here indicate that the chaotic DE is effective in building a good BSNN model for nonlinear identification of an experimental nonlinear yo-yo motion control system.
Leandro dos Santos Coelho, Fabio A. Guerra
Applying Particle Swarm Optimization to Adaptive Controller
Abstract
A design for a model-free learning adaptive control (MFLAC) based on pseudo-gradient concepts and optimization procedure by particle swarm optimization (PSO) is presented in this paper. PSO is a method for optimizing hard numerical functions on metaphor of social behavior of flocks of birds and schools of fish. A swarm consists of individuals, called particles, which change their positions over time. Each particle represents a potential solution to the problem. In a PSO system, particles fly around in a multi-dimensional search space. During its flight each particle adjusts its position according to its own experience and the experience of its neighboring particles, making use of the best position encountered by itself and its neighbors. The performance of each particle is measured according to a pre-defined fitness function, which is related to the problem being solved. The PSO has been found to be robust and fast in solving non-linear, non-differentiable, multi-modal problems. Motivation for application of PSO approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with particle swarm optimization for a nonlinear control valve are showed.
Leandro dos Santos Coelho, Fabio A. Guerra
B-Spline Neural Network Using an Artificial Immune Network Applied to Identification of a Ball-and-Tube Prototype
Abstract
B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods that may fall into local minima during the learning procedure. When using feed-forward BSNNs, the quality of approximation depends on the control points (knots) placement of spline functions. This paper describes the application of an artificial immune network inspired optimization method ( the opt-aiNet ( to provide a stochastic search to adjust the control points of a BSNN. The numerical results presented here indicate that artificial immune network optimization methods useful for building a good BSNN model for the nonlinear identification of an experimental nonlinear ball-and-tube system.
Leandro dos Santos Coelho, Rodrigo Assunção

Part III: Pattern Recognition

Frontmatter
Pattern Recognition for Industrial Security Using the Fuzzy Sugeno Integral and Modular Neural Networks
Abstract
We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a Hierarchical Genetic Algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach.
Patricia Melin, Alejandra Mancilla, Miguel Lopez, Daniel Solano, Miguel Soto, Oscar Castillo
Application of a GA/Bayesian Filter-Wrapper Feature Selection Method to Classification of Clinical Depression from Speech Data
Abstract
This paper builds on previous work in which a feature selection method based on Genetic Programming (GP) was applied to a database containing a very large set of features that were extracted from the speech of clinically depressed patients and control subjects, with the goal of finding a small set of highly discriminating features. Here, we report improved results that were obtained by applying a technique that constructs clusters of correlated features and a Genetic Algorithm (GA) search that seeks to find the set of clusters that maximizes classification accuracy. While the final feature sets are considerably larger than those previously obtained using the GP approach, the classification performance is much improved in terms of both sensitivity and specificity. The introduction of a modified fitness function that slightly favors smaller feature sets resulted in further reduction of the feature set size without any loss in classification performance.
Juan Torres, Ashraf Saad, Elliot Moore
Comparison of PSO-Based Optimized Feature Computation for Automated Configuration of Multi-sensor Systems
Abstract
The design of intelligent sensor systems requires sophisticated methods from conventional signal processing and computational intelligence. Currently, a significant part of the overall system architecture still has to be manually elaborated in a tedious and time consuming process by an experienced designer. Clearly, an automatic method for auto-configuration of sensor systems would be salient. In this paper, we contribute to the optimization of the feature computation step in the overall system design, investigating multi-level thresholding (MLT) and Gaussian windowing. Our goals are to compare these two feature computation methods and two evolutionary optimization techniques, i.e., genetic algorithm (GA) and particle swarm optimization (PSO). To compare with previous research work gas sensor benchmark data is used. In the comparison of GA and PSO the latter method provided superior results of 100% recognition in generalization for thresholding, which proved to be more powerful method.
Kuncup Iswandy, Andreas Koenig
Evaluation of Objective Features for Classification of Clinical Depression in Speech by Genetic Programming
Abstract
This paper presents the results of applying a Genetic Programming (GP) based feature selection algorithm to find a small set of highly discriminating features for the detection of clinical depression from a patient’s speech. While the performance of the GP-based classifiers was not as good as hoped for, several Bayesian classifiers were trained using the features found via GP and it was determined that these features do hold good discriminating power. The similarity of the feature sets found using GP for different observational groupings suggests that these features are likely to generalize well and thus provide good results with other clinical depression speech databases.
Juan Torres, Ashraf Saad, Elliot Moore
A Computationally Efficient SUPANOVA: Spline Kernel Based Machine Learning Tool
Abstract
Many machine learning methods just consider the quality of prediction results as their final purpose. To make the prediction process transparent (reversible), spline kernel based methods were proposed by Gunn. However, the original solution method, termed SUpport vector Parsimonious ANOVA (SUPANOVA) was computationally very complex and demanding. In this paper, we propose a new heuristic to compute the optimal sparse vector in SUPANOVA that replaces the original solver for the convex quadratic problem of very high dimensionality. The resulting system is much faster without the loss of precision, as demonstrated in this paper on two benchmarks: the iris data set and the Boston housing market data benchmark.
Boleslaw K. Szymanski, Lijuan Zhu, Long Han, Mark Embrechts, Alexander Ross, Karsten Sternickel

Part IV: Classification

Frontmatter
Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality
Abstract
We present a multi-dimensional mapping strategy using multiobjective genetic programming (MOGP) to search for the (near-)optimal feature extraction pre-processing stages for pattern classification as well as optimizing the dimensionality of the decision space. We search for the set of mappings with optimal dimensionality to project the input space into a decision space with maximized class separability. The steady-state Pareto converging genetic programming (PCGP) has been used to implement this multi-dimensional MOGP. We examine the proposed method using eight benchmark datasets from the UCI database and the Statlog project to make quantitative comparison with conventional classifiers. We conclude that MMOGP outperforms the comparator classifiers due to its optimized feature extraction process.
Yang Zhang, Peter I Rockett
A Cooperative Learning Model for the Fuzzy ARTMAP-Dynamic Decay Adjustment Network with the Genetic Algorithm
Abstract
In this paper, combination between a Fuzzy ARTMAP-based artificial neural network (ANN) model and the genetic algorithm (GA) for performing cooperative learning is described. In our previous work, we have proposed a hybrid network integrating the Fuzzy ARTMAP (FAM) network with the Dynamic Decay Adjustment (DDA) algorithm (known as FAMDDA) for tackling pattern classification tasks. In this work, the FAMDDA network is employed as the platform for the GA to perform weight reinforcement. The performance of the proposed system (FAMDDA-GA) is assessed by means of generalization on unseen data from three benchmark problems. The results obtained are analyzed, discussed, and compared with those from FAM-GA. The results reveal that FAMDDA-GA performs better than FAM-GA in terms of test accuracy in the three benchmark problems.
Shing Chiang Tan, M. V. C. Rao, Chee Peng Lim
A Modified Fuzzy Min-Max Neural Network and Its Application to Fault Classification
Abstract
The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propose to employ both the membership value of the hyperbox fuzzy sets and the Euclidean distance for classification. To assess the effectiveness of the modified FMM network, benchmark pattern classification problems are first used, and the results from different methods are compared. In addition, a fault classification problem with real sensor measurements collected from a power generation plant is used to evaluate the applicability of the modified FMM network. The results obtained are analyzed and explained, and implications of the modified FMM network in real environments are discussed.
Anas M. Quteishat, Chee Peng Lim
AFC-ECG: An Adaptive Fuzzy ECG Classifier
Abstract
After long-term exploration, it has been well established for the mechanisms of electrocardiogram (ECG) in health monitoring of cardiovascular system. Within the frame of an intelligent home healthcare system, our research group is devoted to researching/developing various mobile health monitoring systems, including the smart ECG interpreter. Hence, in this paper, we introduce an adaptive fuzzy ECG classifier with orientation to smart ECG interpreters. It can parameterize the incoming ECG signals and then classify them into four major types for health reference: Normal (N), Premature Atria Contraction (PAC), Right Bundle Block Beat (RBBB), and Left Bundle Block Beat (LBBB).
Wai Kei Lei, Bing Nan Li, Ming Chui Dong, Mang I Vai
A Self-organizing Fuzzy Neural Networks
Abstract
This paper proposes a novel clustering algorithm for the structure learning of fuzzy neural networks. Our clustering algorithm uses the reward and penalty mechanism for the adaptation of the fuzzy neural networks prototypes at every training sample. Compared with the classical clustering algorithms, the new algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure. No priori knowledge of the input data distribution is needed for initialization. All rules are self-created, and they grow automatically with more incoming data. There are no conflicting rules in the created fuzzy neural networks. Our approach also shows that supervised clustering algorithms can be used for the structure learning of the self-organizing fuzzy neural networks. The identification of several typical nonlinear dynamic systems is developed to demonstrate the effectiveness of this learning algorithm.
Haisheng Lin, X. Z. Gao, Xianlin Huang, Zhuoyue Song

Part V: Soft Computing for Modeling, Optimization and Information Processing

Frontmatter
A Particle Swarm Approach to Quadratic Assignment Problems
Abstract
Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. But research on the Quadratic Assignment Problem (QAP) has not much been investigated. In this paper, we introduce a novel approach based on PSO for QAPs. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. A new mapping is proposed between the particles in the swarm and the problem space in an efficient way. We evaluate the performance of the proposed approach with Ant Colony Optimization (ACO) algorithm. Empirical results illustrate that the approach can be applied for solving quadratic assignment problems and it has outperforms ACO in the completion time.
Hongbo Liu, Ajith Abraham, Jianying Zhang
Population-Based Incremental Learning for Multiobjective Optimisation
Abstract
The work in this paper presents the use of population-based incremental learning (PBIL), one of the classic single-objective population-based optimisation methods, as a tool for multiobjective optimisation. The PBIL method with two different updating schemes of its probability vectors is presented. The performance of the two proposed multiobjective optimisers are measured and compared with four other established multiobjective evolutionary algorithms i.e. niched Pareto genetic algorithm, version 2 of non-dominated sorting genetic algorithm, version 2 of strength Pareto evolutionary algorithm, and Pareto archived evolution strategy. The optimisation methods are implemented to solve 8 bi-objective test problems where design variables are encoded as a binary string. The Pareto optimal solutions obtained from the various methods are compared and discussed. It can be concluded that, with the assigned test problems, the multiobjective PBIL methods are comparable to the previously developed algorithms in terms of convergence rate. The clear advantage in using PBILs is that they can provide considerably better population diversity.
Sujin Bureerat, Krit Sriworamas
Combining of Differential Evolution and Implicit Filtering Algorithm Applied to Electromagnetic Design Optimization
Abstract
Differential evolution (DE) is a population-based and stochastic search algorithm of evolutionary computation that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. This work presents a global optimization algorithm based on DE approaches combined with local search using the implicit filtering algorithm. The implicit filtering algorithm is a projected quasi-Newton method that uses finite difference gradients. The difference increment is reduced as the optimization progresses, thereby avoiding some local minima, discontinuities, or nonsmooth regions that would trap a conventional gradient-based method. Problems involving optimization procedures of complex mathematical functions are widespread in electromagnetics. Many problems in this area can be described by nonlinear relationships, which introduce the possibility of multiple local minima. In this paper, the shape design of Loney’s solenoid benchmark problem is carried out by DE approaches. The results of DE approaches are also investigated and their performance compared with those reported in the literature.
Leandro dos Santos Coelho, Viviana Cocco Mariani
A Layered Matrix Cascade Genetic Algorithm and Particle Swarm Optimization Approach to Thermal Power Generation Scheduling
Abstract
A layered matrix encoding cascade genetic algorithm and particle swarm optimization approach (GA-PSO) for unit commitment and economic load dispatch problem in a thermal power system is presented in this paper. The tasks of determining and allocating power generation to different thermal units in a way that the total power production cost is at the minimum subject to equality and inequality constraints makes unit commitment and economic load dispatch challenging. A case study, based on the thermal power generation problem presented in [1], is used to demonstrate the effectiveness of the proposed method in generating a cost-effective power generation schedule. The schedule obtained is compared with that of Linear Programming (LP) as reported in [1]. The results show that the proposed GA-PSO approach outperforms LP in solving the unit commitment and economic load dispatch problem for thermal power generation system in this case study.
Siew Chin Neoh, Norhashimah Morad, Chee Peng Lim, Zalina Abdul Aziz
Differential Evolution for Binary Encoding
Abstract
Differential Evolution (DE) is a competitive optimization technique for numerical optimization problems with real-parameter representation. This paper aims to investigate how DE can be adapted with binary encoding and to study its behaviors on the binary level.
Tao Gong, Andrew L. Tuson

Part VI: Soft Computing in Civil Engineering and Other Applications

Frontmatter
Prioritization of Pavement Stretches Using Fuzzy MCDM Approach – A Case Study
Abstract
Effective pavement management requires the prioritization of the road stretches for logical disbursement of the funds available towards maintenance of the pavement. Several methods have been developed and implemented towards this goal. However, the uncertainty involved with some of the parameters has not been addressed adequately in most of the works. One such parameter has been identified as the severity of distress which is difficult to assess accurately. Hence a Fuzzy Multi Criteria Decision Making (FMCDM) approach has been proposed in this paper. For demonstration of the approach, pavement distresses with respect to their extent and severity have been collected over a number of stretches. In addition, an expert opinion survey has been carried out to quantify the influence of these parameters on the functional condition of the pavement. Priority Index (PI) has been worked out, based on which the ranking of the stretches has been arrived at.
A. K. Sandra, V. R. Vinayaka Rao, K. S. Raju, A. K. Sarkar
A Memetic Algorithm for Water Distribution Network Design
Abstract
The majority of real optimization problems cannot be solved exactly because they have very large and highly complex search spaces. One of these complex problems is the design of looped water distribution networks, which consists of determining the best way of conveying water from the sources to the users, satisfying their requirements. This paper is to present a new memetic algorithm and evaluate its performance in this problem. With the aim to establish an accurate conclusion, other four heuristic approaches have also been adapted, including simulated annealing, mixed simulated annealing and tabu search, iterated local search, and scatter search. Results obtained in two water distribution networks demonstrate that the memetic algorithm works better when the size of the problem increases.
R. Baños, C. Gil, J. I. Agulleiro, J. Reca
Neural Network Models for Air Quality Prediction: A Comparative Study
Abstract
The present paper aims to find neural network based air quality predictors, which can work with limited number of data sets and are robust enough to handle data with noise and errors. A number of available variations of neural network models such as Recurrent Network Model (RNM), Change Point detection Model with RNM (CPDM), Sequential Network Construction Model (SNCM), and Self Organizing Feature Maps (SOFM) are implemented for predicting air quality. Developed models are applied to simulate and forecast based on the long-term (annual) and short-term (daily) data. The models, in general, could predict air quality patterns with modest accuracy. However, SOFM model performed extremely well in comparison to other models for predicting long-term (annual) data as well as short-term (daily) data.
S. V. Barai, A. K. Dikshit, Sameer Sharma
Recessive Trait Cross over Approach of GAs Population Inheritance for Evolutionary Optimization
Abstract
This research presents an investigation into a new population inheritance approach using a concept taken from the recessive trait idea for evolutionary optimization. Evolutionary human inheritance recessive trait idea is used to enhance the effectiveness of the traditional genetic algorithms. The capability of the modified approach is explored by two examples (i) a mathematical function of two variables, and (ii) an active vibration control of a flexible beam system. Finally, a comparative performance for convergence is presented and discussed to demonstrate the merits of the modified genetic algorithms approach over the traditional ones.
Amr Madkour, Alamgir Hossain, Keshav Dahal
Automated Prediction of Solar Flares Using Neural Networks and Sunspots Associations
Abstract
An automated neural network-based system for predicting solar flares from their associated sunspots and simulated solar cycle is introduced. A sunspot is the cooler region of the Sun’s photosphere which, thus, appears dark on the Sun’s disc, and a solar flare is sudden, short lived, burst of energy on the Sun’s surface, lasting from minutes to hours. The system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots and flares. Size, shape and spot density of relevant sunspots are used as input values, in addition to the values found by the solar activity model introduced by Hathaway. Two outputs are provided: The first is a flare/ no flare prediction, while the second is type of the solar flare prediction (X or M type flare). Our system provides 91.7% correct prediction for the possible occurrences and, 88.3% correct prediction for the type of the solar flares.
T. Colak, R. Qahwaji
Backmatter
Metadata
Title
Soft Computing in Industrial Applications
Editors
Ashraf Saad
Keshav Dahal
Muhammad Sarfraz
Rajkumar Roy
Copyright Year
2007
Publisher
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-70706-6
Print ISBN
978-3-540-70704-2
DOI
https://doi.org/10.1007/978-3-540-70706-6

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