Recent Developments and New Direction in Soft-Computing Foundations and Applications

In this paper, research work allows making a statement which can be formulated as complex of models and methods. These models and methods are able to take on the function of whole intellectual complex which carries out the function of teaching and process of control knowledge with the minimal participation of teacher and education institutions.

The work presents the results of practical realization of database and methods. These results are the basic main element on which the work was done to determine the practical efficiency of application intelligent systems in learning process.

The paper describes the necessity of application of intelligent technologies to support decision making in human resource management (HRM) problems. The specific features of the personnel selection problem are highlighted, immersing the later into a fuzzy environment. Multi-scenario approach is described for solving the problem of employment, taking into account the importance and in equivalence of the indicators, which characterize the eligible candidates for the post, as well as individual character requirements of employers, at a current time. Experiment results for implementing the problem of selection of personnel based on the proposed method for professionals in information technology (IT) are discussed.

The context analysis of customer requests in a natural language call routing problem is investigated in this paper. Understanding of customer intention is one of the most important problems in natural language call routing. The adaptive neuro-fuzzy inference system is examined for solving this problem. This system can be applied to any language call routing domain; that is, there is no lexical or syntactic analysis used in the classification.

Naturally dealing with information processing tasks such as database querying, information retrieval, and decision support requires the adequate handling of bipolarity that might be present in the specification of user preferences in selection criteria. Indeed, past and recent research revealed that users often express their preferences for criteria specifications in a bipolar and sometimes even multipolar way.

In this paper, a new method for response integration, based on the Choquet integral with interval type 2 Sugeno measures, is presented. Type 1 and interval type 2 fuzzy systems for edge detection based on the Sobel and morphological gradient are used, which is a preprocessing system applied to the training data for better performance in the modular neural network. Fuzzy Sugeno measures are represented by an interval type 2 fuzzy system. The Choquet integral is used as a method to integrate the outputs of the modules of the modular neural networks (MNN). A database of faces was used to perform the preprocessing, the training, and the combination of information sources of the MNN.

Analyzing how people actually make decisions, the Nobelist Daniel Kahneman and his co-author Amos Tversky found out that instead of maximizing the expected gain, people maximize a weighted gain, with weights determined by the corresponding probabilities. The corresponding empirical weights can be explained qualitatively, but quantitatively, these weights remain largely unexplained. In this paper, we show that with a surprisingly high accuracy, these weights can be explained by fuzzy logic ideas.

One of the main questions in product life cycle management is how to create the comprehensive framework for autonomous, intelligent decision-making which integrates business and scheduling data. The key problem is to simulate human-like decision-making process to provide agile manufacturing process. Multiagent technologies play a key role in this problem and form an integration platform between human and manufacturing. Also developing distributed control system based on multiagent technologies encounters difficulties, due to ambiguous, vague, or missing information. In the area of intelligent manufacturing systems, there are a number of fuzzy scheduling models presented (Subramaniam et al. in Int J Adv Manuf Technol 16(10): 759–764, [1]; Srinoi et al. in Int J Prod Res 44(11): 1–21, [2]). These frameworks only deal with manufacturing processes. The research presents multiagent framework that integrates design, manufacturing, and control process in fuzzy area using resource-based approach to agent’s interaction. This model is applicable to work with various manufacturing agents in conjunction with different design agents and control systems.

Digital image processing can often improve the quality of visual sensing of images and real-world scenes. Recently, high dynamic range (HDR) imaging techniques have become more and more popular in the field. Both classical and soft computing–based methods proved to be advantages in revealing the non-visible parts of images and realistic scenes. However, extracting as much details as possible is not always enough because the sensing capability of the human eye depends on many other factors and the visual quality is not always proportional to the rate of accurate reproduction of the scene. In this paper, a new fuzzy information measure is introduced by which the quality of HDR images can be improved and both the amount of visible details and the quality of sensing can be increased.

In this paper, a new method of fuzzy inference system optimization using a hierarchical genetic algorithm (HGA) is proposed. The fuzzy inference system is used to combine the different responses of modular neural networks (MMNs). In this case, the MMNs are used to perform the human recognition using 4 biometric measures: face, iris, ear, and voice. The main idea is the optimization of some parameters of a fuzzy inference system such as the type of fuzzy logic (FL), type of system, number of membership functions in each input, type of membership functions in each variable, their parameters, and the consequences of the fuzzy rules.

This paper presents the optimization of the antecedent parameters for a system of edge detection based on Sobel technique combined with interval type-2 fuzzy logic. For the optimization of the fuzzy inference system, the cuckoo search (CS) algorithm is applied, the idea is to find the design parameters of an IT2-FLS and achieve better results in applications of edge detection for digital images.

In this paper, the results of meta-heuristic optimization techniques with various parameter settings are presented. A formerly published Fuzzy-Based Recognizer (FUBAR): A fuzzy rule-based classification algorithm was used to analyze and evaluate the behavior of the used meta-heuristic optimization algorithms for rule-base optimization. Besides the reached accuracy, the execution time, the CPU load of the algorithms, and the effects of the shapes of the fuzzy membership functions in the initial rule-base are also investigated.

This work describes a learning vector quantization (LVQ) method for unsupervised neural networks for classification tasks. We work with a modular architecture of this method, so we can classify three classes per module. We also work with three different databases, the arrhythmia database from MIT-BIH, which contains 15 different classes, a character database from UCI with 26 different classes, and finally a vehicle silhouettes database also from UCI with 4 different classes.

In this paper, we present the extension of the fuzzy possibilistic C-means (FPCM) algorithm using type-2 fuzzy logic techniques, with the goal of improving the performance of this algorithm. We also performed the comparison of this proposed algorithm against the interval type-2 fuzzy C-means (IT2FCM) algorithm to observe whether the proposed approach performs better than this algorithm. The proposed extension was realized considering both of the weight exponents (fuzzy and possibilistic), m and η, as interval fuzzy sets.

Everyday, the users use the Web for things of their interest. They expect to find items that precisely, to the highest possible degree, match the items they are looking for. Quite often this is not enough, they would like to be exposed to things that provide them with some novelty. Systems that support users in their search activities provide them with some kind of variation, but it is not a controlled process. Diversity is accidental—the systems try to estimate what items users may like based on similarities between users, users’ activities, or on explicitly specified preferences. The users do not have any influence on conditions governing formation of lists of suggested items. In this paper, we assert that application of fuzziness in systems supporting users in their search activities will allow the users to overlook and control mechanisms that identify alternatives and options suggested to them, as well as to influence selection of individuals that constitute groups providing suggestions. We focus on two applications of fuzzy methods that ensure controllable selection processes and illustrate benefits of fuzzy-based processing of available information. Firstly, we concentrate on social networks. A methodology for selecting groups of individuals that satisfy linguistically described requirements regarding the degree of matching between users’ interests and collective interests of groups is presented. Secondly, we offer a novel recommending approach that provides users with a fuzzy-based process aiming at construction of lists of suggested items. This is accomplished via explicit control of requirements regarding rigorousness of identifying users who become a reference base for generating suggestions. A new way of ranking items rated by multiple users based on Pythagorean fuzzy sets (PFS) and taking into account not only assigned rates but also their number is described.

The methods of construction of non-statistical association measures on the sets with involution operation and similarity measure are proposed. The Pearson’s correlation coefficient is obtained as a particular case of the class of association measures associated with Lukasiewicz t-conorm. Examples of association measures on [0, 1] and on the set of fuzzy sets are considered.

Nowadays, it has become a common practice to provide public access to various kinds of primary non-aggregated statistical data. Necessary precautions ought to be taken in order to guarantee that sensitive data features are masked, and data privacy cannot be violated. In the case of protecting information about a group of people, it is important to protect intrinsic data features and distributions. To do so, it is obligatory to introduce a certain level of distortion into the dataset. The problem of minimizing this distortion is a complex optimization task, which can be successfully solved by applying appropriate heuristic procedures, e.g., memetic algorithms. The task of determining whether a particular solution masks sensitive data features is an ill-defined one and often can be solved only by expert evaluation. In the paper, we propose to apply two-phase memetic algorithm to solve such tasks of providing group anonymity, for which it is not always possible to define appropriate constraints.

Cyber-networks are characterized by two distinct types of nodes—devices and users—and by voluminous interactions in real time. All computational intelligence approaches to the analysis of these networks must deal with complexities of these interactions and their high data rates. This paper describes a computationally feasible approach to querying large cyber-networks using word-based queries. The attribute memberships and connection strengths in these cyber-networks are described granularly using appropriate vocabularies of words, where the words themselves are modeled using interval type-2 (IT2) fuzzy membership functions (MF) on an appropriate scale. By employing precomputation and storage of these word representations and queries, automated monitoring functions in large cyber-networks can be performed in real time via simple arithmetic calculations. We provide an illustrative example using data from a real cyber-network.

The concept of analysis of the information in Big Data is offered. In the proposed concept, it is introduced some universal set of values with the limited number of words. All files (sources of information) projected into the universal set. The search purpose was formed in terms of universal set. Then, search process was performed in the universal set, i.e. in the set of projection of sources of information. Such technology reduces the localization of searching information. Such approach allows locate the required information within the framework of the traditional sizes and makes possible further application of methods and algorithms of the information processing for them.

To overcome some problems with deep understanding of fuzzy values, certain learning finite automaton was put into a fuzzy environment. Previously, such a device has been studied in the probabilistic environment, where the classic technique of standard Markov chains was applicable. The new study became possible due to several previous results by the present author, namely the axiomatic of fuzzy evidence accumulation and the theory of generalized Markov chains. The mathematical results, obtained in the paper, prove that the learning automaton has the property of asymptotic optimality. We propose to use this property for measuring membership functions in case of values analogous to singletons or point functions. It is claimed that the obtained results might lead to a fuzzy value measurement procedure resembling statistics developed in probability area.

The report attempts to make a comparative analysis of ChP- and SP-based information approach and to identify the factors that cause the occurrence of ChP solutions in deterministic equations.

Last decade has seen constant growth in wireless technology. Still there is requirement for higher data rates. Current technologies have nearly maximized the use of temporal and spectral techniques to improve capacity and data transfer speeds. But additional spatial dimension is not yet exploited. We can improve capacity of cellular systems by canceling interfering signals using directional arrays. This process is known as beamforming. There are numerous studies available for beamforming mostly using uniform linear arrays but little work has been done on other array configurations. Constrained beamforming techniques with planar array configurations are to be developed for capacity improvement of wireless systems in 3D space. In this work, we have employed the bacterial foraging optimization algorithm (BFOA) and genetic algorithm (GA) for constrained beamforming using uniform planar array and uniform circular arrays.

This paper shows the use of particle swarm optimization (PSO) in the design of a neural network ensemble with type-1 and type-2 fuzzy integration of responses for time series prediction. The considered time series in this paper for testing the hybrid approach is the US/Dollar MX time series. Simulation results show that the hybrid ensemble approach, combining neural networks and fuzzy logic, produces good prediction of the dollar time series.

This paper describes the Mackey-Glass time series prediction using genetic optimization of type-1 and interval type-2 fuzzy integrators in Ensembles of adaptive neuro-fuzzy inferences systems (ANFIS) models, with emphasis on its application to the prediction of chaotic time series. The considered chaotic problem is the Mackey-Glass time series that is generated from the differential equations, so this benchmark time series is used to the test of performance of the proposed Ensemble architecture. We used the interval type-2 and type-1 fuzzy systems to integrate the outputs (forecasts) of each of the ANFIS models in the Ensemble. Genetic algorithms (GAs) were used for the optimization of memberships function (with linguistic labels “Small, Middle, and Large”) parameters of the fuzzy integrators. In the experiments, the GAs optimized the Gaussians, generalized bell and triangular membership functions for each of the fuzzy integrators, thereby increasing the complexity of the training. Simulation results show the effectiveness of the proposed approach.

Modern times, highly competitive, global operating environment, sustainability plays a very vital role. Changing climatic conditions and environmental deterioration has multi dimensional impact on every sphere of life forms and life form driving processes. Public hold on corporations responsible for ecological misconduct in their supply chains getting more firm with time. To counter the threat it’s a high time industries started taking initiatives for sustainability in their supply chains. In spite of that, suppliers often are unsuccessful to appropriately contribute in these initiatives. Hence, present paper justifies the supplier involvement in sustainable initiatives in supply chain management (SCM) with using differential evolution (DE) to select the efficient sustainable suppliers providing the maximum fulfillment for the sustainable criteria determined. Finally, two illustrative cases on automotive industry validate the application of the present approach.

This paper presents an analysis of the effects in quality results that bring the use of different types of membership functions in an interval type-2 fuzzy system, used to adapt some parameters of particle swarm optimization (PSO). Benchmark mathematical functions are used to test the methods, and a comparative study is performed.

We propose a technology for calculating robust correlation matrices and robust normalized correlation matrices to indicate the beginning of the latent period of the emergency state of technological objects. For the same purpose, we also propose a technology for calculating estimates of characteristics of noise and useful signal, which assumes zero values in the normal state. Sets of informative attributes are formed from them in monitoring systems for industrial objects; while the object is in service, the number and value of the nonzero element are used to determine the location and nature of failure.

In this paper, we show a fuzzy control optimization using genetic algorithms, and this optimization helps us to improve the flight control of an airplane. To control the flight control of the airplane, fuzzy systems were used to control the stability of the airplane. In this paper, the fuzzy systems and the behavior of the airplane are explained to understand the complete work.

Recently, bio-inspired methods have become powerful optimization algorithms to solve complex problems. We also mention alternative approaches without optimization techniques for obtaining the controller. Swarm intelligence is the part of artificial intelligence based on the study of actions of individuals in various decentralized systems. The main objective of the work is based on the main reasons for the optimization of the classical control of type Mamdani in the fuzzy controller, specifically in tuning membership functions of the fuzzy controller for the benchmark problem known as the water tank using two methods of optimization a simple ant colony optimization (S-ACO) and the bee colony optimization (BCO) for membership functions’ parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for a benchmark problem known as the water tank. Finally, we provide a comparison of both methods for the case of designing of the classical control of type Mamdani in the fuzzy controllers.

This paper presents the development of the mathematical model with fuzzy parametrical identification of the ecopyrogenesis (EPG) complex reactor as a temperature control object. The synthesis procedure of the fuzzy parametrical identification system of Mamdani type is presented. The analysis of computer simulation results in the form of static and dynamic characteristic graphs of the reactor as a temperature control object confirms the high adequacy of the developed model to the real processes. The developed mathematical model with fuzzy parametrical identification gives the opportunity to investigate the behavior of the temperature control object in steady and transient modes, in particular, to synthesize and adjust the temperature controller of the reactor temperature automatic control system (ACS).

The paper is devoted to the synthesis of digital system for control of thermoacoustic plant. Based on the analysis of thermoacoustic systems, the main tasks of the synthesized system are shown. Its structure and main components are described. Using the created system, the traditional PD and fuzzy Mamdani and Sugeno controllers are implemented and compared. Best regulator is then additionally tuned using the described input terms optimization procedure. The comparative analysis of initial and optimized controllers is shown using graphs.

Wireless LAN (WLAN) is considered having “point-to-point” mode, consisting of an information maintenance device (IMD) and subscriber stations distributed across multiple identical subnets interacting by a common wireless radio link. Basing on Laplace transform, analytical models of service and information delivery processes at the stations of the network subnets have been developed. Methods for calculating probability-time characteristics of the service and information delivery processes at the stations of the subnets and in the network as a whole have been proposed.

We present here the new approach for design and modeling evaluation of economic clusters based on artificial neural networks platform. We show here the basic principles and discuss the application of the approach for Hopfield networks.

The objective of designing and installation air quality monitoring network (AQMN) is to reduce network density with a view to acquire maximum information on air quality with minimum expenses. In spite of the best research efforts, there has been no general acceptance of any method for deciding the number of stations. Majority of the cities have, therefore, installed monitoring stations with their own guidelines. The present paper presents a useful formulation for classification of the existing air quality monitoring stations (AQMS) using fuzzy similarity measures. The case study has been demonstrated by applying the methodology to the already-installed AQMS in Delhi, India.

In real life, imperfect information is commonly present in all the components of the decision-making problem. In decision-making problems, a DM is almost never provided with perfect, that is, ideal decision-relevant information to determine states of nature, outcomes, probabilities, utilities, etc. We are known that relevant information almost always comes imperfect. Imperfect information is information in which one or more respects are imprecise, uncertain, incomplete, unreliable, vague, or partially true [1]. Two main concepts of imperfect information are imprecision and uncertainty. Imprecision is one of the widest concepts including variety of cases. We will discuss uncertainty concepts of imperfect information and its application for problem modeling of decision maker. In the first stage of the modeling, the identification determinants of a decision maker was implemented using Delphi method. The aim of the second stage consisted of the linguistic evaluation of the factors. At the final stages, decision-maker model was realized by using possibility–probability-based method and Dempster–Shafer theory-based model.

Maintenance of large engineering systems is a complex requirement. Experience shows that a combination of both time- and condition-based maintenance is required to be optimally planned for such systems. Further, such a plan is required to be put in place even as the systems are being designed and installed so that the benefits of maintenance are maximized. While it is possible to use historical data for reliability and maintenance models, considerable amount of knowledge available as domain expertise needs to be tapped, to effectively model and plan maintenance strategies. In this paper, a framework for integration of time- and condition-based maintenance is presented and areas where domain expertise can be harnessed have been highlighted. Fuzzy logic has been shown as a useful tool in this framework to elicit expert information. A case study has been discussed to demonstrate the utility of expert information in modeling and planning scheduled preventive maintenance aspect for a ship’s machinery platform.