Computational Intelligence Systems and Applications

This book presents new concepts and implementations of Computational Intelligence (CI) systems (based on neuro-fuzzy and fuzzy neural synergisms) and a broad comparative analysis with the best-known existing neuro-fuzzy systems as well as with systems representing other knowledge-discovery techniques such as rough sets, decision trees, regression trees, probabilistic rule induction etc. This presentation is preceded by a discussion of the main directions of synthesizing fuzzy sets, artificial neural networks and genetic algorithms in the framework of designing CI systems. In order to keep the book self-contained, introductions to the basic concepts of fuzzy systems, artificial neural networks and genetic algorithms are given. This book is intended for researchers and practitioners in AI/CI fields and for students of computer science or neighbouring areas.

Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge "buried" in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge "encoded" in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.

Self-contained presentation of new concepts and structures of CI systems and their real-life applications Includes supplementary material: sn.pub/extras

Contenu
1 Introduction.- 1.1 A general concept of computational intelligence.- 1.2 The building blocks of computational intelligence systems.- 1.3 Objectives and scope of this book.- 2 Elements of the theory of fuzzy sets.- 2.1 Basic notions, operations on fuzzy sets, and fuzzy relations.- 2.2 Fuzzy inference systems.- 3 Essentials of artificial neural networks.- 3.1 Processing elements and multilayer perceptrons.- 3.2 Radial basis function networks.- 4 Brief introduction to genetic algorithms.- 4.1 Basic components of genetic algorithms.- 4.2 Theoretical introduction to genetic computing.- 5 Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems.- 5.1 Artificial intelligence versus computational intelligence.- 5.2 Designing computational intelligence systems.- 5.3 Selected neuro-fuzzy systems.- 6 Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data.- 6.1 Synthesizing rule-based knowledge from data statement of the problem.- 6.2 Neuro-fuzzy system in learning mode problem of knowledge acquisition.- 6.3 Neuro-fuzzy system in inference mode approximate inference engine.- 6.4 Learning techniques.- 6.5 A numerical example of synthesizing rule-based knowledge from data modelling the Mackey-Glass chaotic time series.- 6.6 Synthesizing rule-based knowledge from fish data.- 7 Rule-based neuro-fuzzy modelling of dynamic systems and designing of controllers.- 7.1 System identification statement of the problem and its general solution in the framework of neuro-fuzzy methodology.- 7.2 Rule-based neuro-fuzzy modelling of an industrial gas furnace system.- 7.3 Designing the neuro-fuzzy controller for a simulated backing up of a truck.- 8Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support.- 8.1 Designing the classifier from data statement of the problem.- 8.2 Learning mode of neuro-fuzzy classifier.- 8.3 Inference (decision making) mode of neuro-fuzzy classifier.- 8.4 Neuro-fuzzy decision support system for diagnosing breast cancer.- 8.5 Neuro-fuzzy-genetic decision support system for the glass identification problem (forensic science).- 8.6 Neuro-fuzzy-genetic decision support system for determining the age of abalone (marine biology).- 9 Fuzzy neural network for system modelling and control.- 9.1 Learning mode of the network.- 9.2 Inference mode of the network.- 9.3 Fuzzy neural modelling of dynamic systems (an industrial gas furnace system).- 9.4 Fuzzy neural controller.- 10 Fuzzy neural classifier.- 10.1 Learning and inference modes of the classifier.- 10.2 Fuzzy neural classifier for diagnosis of surgical cases in the domain of equine colic.- A Appendices.- A.1.1 Inputs.- A.1.2 Output.- A.2.1 Inputs.- A.2.2 Outputs set of two class labels.- A.3.1 Inputs.- A.3.2 Outputs set of two class labels.- A.4.1 Inputs.- A.4.2 Outputs set of three class labels.- A.5.1 Inputs.- A.5.2 Outputs three sets of class labels.- References.