What Should Be Computed To Understand And Model Brain Function From Robotics Soft Computing Biology And Neuroscience To Cognitive Philosophy PDF Download

Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic human intelligence in thinking, learning, reasoning, and many other aspects. Knowledge engineering (KE), which deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance, has made significant progress recently, owing to the indefatigable efforts of researchers. Undoubtedly, the hot topics of data mining and knowledge/data discovery have injected new life into the classical AI world. This book tells readers how KE has been influenced and extended by SC and how SC will be helpful in pushing the frontier of KE further. It is intended for researchers and graduate students to use as a reference in the study of knowledge engineering and intelligent systems. The reader is expected to have a basic knowledge of fuzzy logic, neural networks, genetic algorithms, and knowledge-based systems. Contents: Knowledge Engineering and Soft Computing OCo An Introduction (L-Y Ding); Fuzzy Knowledge-Based Systems: Linguistic Integrity: A Framework for Fuzzy Modeling OCo AFRELI Algorithm (J Espinosa & J Vandewalle); A New Approach to Acquisition of Comprehensible Fuzzy Rules (H Ohno & T Furuhashi); Fuzzy Rule Generation with Fuzzy Singleton-Type Reasoning Method (Y Shi & M Mizumoto); Antecedent Validity Adaptation Principle for Table Look-Up Scheme (P-T Chan & A B Rad); Fuzzy Spline Interpolation in Sparse Fuzzy Rule Bases (M F Kawaguchi & M Miyakoshi); Revision Principle Applied for Approximate Reasoning (L-Y Ding et al.); Handling Null Queries with Compound Fuzzy Attributes (S-L Wang & Y-J Tsai); Fuzzy System Description Language (K Otsuka et al.); Knowledge Representation, Integration, and Discovery by Soft Computing: Knowledge Representation and Similarity Measure in Learning a Vague Legal Concept (M Q Xu et al.); Trend Fuzzy Sets and Recurrent Fuzzy Rules for Ordered Dataset Modelling (J F Baldwin et al.); Approaches to the Design of Classification Systems from Numerical Data and Linguistic Knowledge (H Ishibuchi et al.); A Clustering Based on Self-Organizing Map and Knowledge Discovery by Neural Network (K Nakagawa et al.); Probabilistic Rough Induction (J-Z Dong et al.); Data Mining via Linguistic Summaries of Databases: An Interactive Approach (J Kacprzyk & S Zadrozny); and other papers. Readership: Graduate students, researchers and lecturers in knowledge engineering and soft computing."