Tree-Structure based Hybrid Computational Intelligence

Theoretical Foundations and Applications

verfasst von: Yuehui Chen, Ajith Abraham

Verlag: Springer Berlin Heidelberg

Buchreihe : Intelligent Systems Reference Library

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved.

This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable.

Inhaltsverzeichnis

Frontmatter

Foundations of Computational Intelligence

Frontmatter

Foundations of Computational Intelligence

Abstract

The field of computational intelligence has evolved with the objective of developing machines that can think like humans. Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers.

Yuehui Chen, Ajith Abraham

Flexible Neural Trees

Frontmatter

Flexible Neural Tree: Foundations and Applications

Abstract

Artificial neural networks (ANNs) have been successfully applied to a number of scientific and engineering fields in recent years, i.e., function approximation, system identification and control, image processing, time series prediction [58]. A neural network’s performance is highly dependent on its structure. The interaction allowed between the various nodes of the network is specified using the structure only. An artificial neural network structure is not unique for a given problem, and there may exist different ways to define a structure corresponding to the problem. Depending on the problem, it may be appropriate to have more than one hidden layer, feedforward or feedback connections, or in some cases, direct connections between input and output layer.

Yuehui Chen, Ajith Abraham

Hierarchical Neural Networks

Frontmatter

Hierarchical Neural Networks

Abstract

Soft Computing (SC), including Neural Computing (NC), Fuzzy Computing (FC), Evolutionary Computing (EC) etc., provides us with a set of flexible computing tools to perform approximate reasoning, learning from data and search tasks. Moreover, it has been observed that the highly increasing computing power and technology, could make possible the use of more complex intelligent architectures, taking advantage of more than one intelligent techniques, not in a competitive, but rather in a collaborative sense. Therefore, discovering of more sophisticated and new evolutionary learning models and its application to new areas and problems still remain as key questions for the next 10 years.

Yuehui Chen, Ajith Abraham

Hierarchical Fuzzy Systems

Frontmatter

Hierarchical Fuzzy Systems

Abstract

Fuzzy inference systems [200], [201], [225] have been successfully applied to a number of scientific and engineering problems during recent years. The advantage of solving complex nonlinear problems by utilizing fuzzy logic methodologies is that the experience or expert’s knowledge described as the fuzzy rule base can be directly embedded into the system for dealing with the problems. Many efforts have been made to enhance systematic design of fuzzy logic systems [203], [204], [205], [206], [207], [239], [244]. Some research focus on automatically finding the appropriate structure and parameters of fuzzy logic systems by using genetic algorithms [204], [207], [239], evolutionary programming [206], tabu search [208], and so on. There are many research works focusing on partitioning of the input space, to determine the fuzzy rules and parameters evolved in the fuzzy rules for a single fuzzy system [232], [229]. As it is well known, the curse-of-dimensionality is an unsolved problem in the fields of fuzzy and/or neuro-fuzzy systems [243].

Yuehui Chen, Ajith Abraham

Reverse Engineering of Dynamical Systems

Frontmatter

Reverse Engineering of Dynamic Systems

Abstract

The general task of system identification problem is to approximate automatically the behavior of an unknown plant using an appropriate model. Identification of nonlinear system suffers many problems including determination of the structure and parameters of the system. Many traditional methods of system identification are based on parameter estimation, and mainly rely on least mean-squares (LMS) method. Recently soft computing based system identification approaches, i.e., neural networks and fuzzy systems have been an active research area.

Yuehui Chen, Ajith Abraham

Conclusions and Future Research

Frontmatter

Concluding Remarks and Further Research

Abstract

Real-world problems are typically ill-defined systems, difficult to model and with large-scale solution spaces. In these cases, precise models are impractical, too expensive, or non-existent. The relevant available information is usually in the form of empirical prior knowledge and input-output data representing instances of the system’s behavior [290]. Soft Computing (SC), including Neural Computing (NC), Fuzzy Computing (FC), Evolutionary Computing (EC) etc., provides us with a set of flexible intelligent computational tools to perform approximate reasoning, learning from data, search tasks etc.

Yuehui Chen, Ajith Abraham

Backmatter

Titel: Tree-Structure based Hybrid Computational Intelligence
verfasst von: Yuehui Chen
Ajith Abraham
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-04739-8
Print ISBN: 978-3-642-04738-1
DOI: https://doi.org/10.1007/978-3-642-04739-8