Riassunto:
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.
Contenuti:
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.- 5.3.1 ANFIS system.- 5.3.2 NEFCLASS system.- 5.3.3 NEFPROX system.- 5.3.4 Neuro-fuzzy system of [242].- 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.- 6.2.1 Conceptual scheme of the system.- 6.2.2 Implementation of the system.- 6.3 Neuro-fuzzy system in inference mode ― approximate inference engine.- 6.3.1 Concept of the system.- 6.3.2 Implementation of the system.- 6.3.3 Testing and pruning the system.- 6.4 Learning techniques.- 6.4.1 Backpropagation-like method.- 6.4.2 Optimization techniques.- 6.4.2.1 Conjugate-gradient algorithm.- 6.4.2.2 Variable-metric algorithm.- 6.4.3 Genetic algorithms.- 6.5 A numerical example of synthesizing rule-based knowledge from data ― modelling the Mackey-Glass chaotic time series.- 6.5.1 Designing the neuro-fuzzy model from data.- 6.5.2 A comparative analysis with several alternative modelling techniques.- 6.6 Synthesizing rule-based knowledge from “fish data”.- 6.6.1 Designing the neuro-fuzzy-genetic system from data.- 6.6.2 A comparison with other methodologies.- 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.- 7.2.1 Designing the neuro-fuzzy model of dynamic system from data.- 7.2.2 A comparative analysis with several alternative methodologies.- 7.3 Designing the neuro-fuzzy controller for a simulated backing up of a truck.- 7.3.1 Designing the controller from data.- 7.3.2 A comparison of different neuro-fuzzy controllers.- 8 Neuro-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.- 8.2.1 Conceptual scheme of the classifier.- 8.2.2 Implementation of the classifier.- 8.3 Inference (decision making) mode of neuro-fuzzy classifier.- 8.3.1 Concept of the system and its implementation.- 8.3.2 Testing and pruning the system.- 8.4 Neuro-fuzzy decision support system for diagnosing breast cancer.- 8.4.1 Designing the system from data.- 8.4.2 A comparative analysis of several different methodologies applied to diagnosing breast cancer.- 8.5 Neuro-fuzzy-genetic decision support system for the glass identification problem (forensic science).- 8.5.1 Designing the system from data.- 8.5.2 A comparative analysis with other techniques for decision support systems design.- 8.6 Neuro-fuzzy-genetic decision support system for determining the age of abalone (marine biology).- 8.6.1 Designing the system from data.- 8.6.2 A comparative analysis with alternative approaches.- 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.- 9.4.1 Structure, learning and operation of the controller.- 9.4.2 A numerical example of fuzzy neural control.- 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.
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