Book — 1 online resource (xi, 196 pages) : illustrations Digital: text file.PDF.
Preface.- Acknowledgements.- Chapte
r1: Introduction.- Chapte
r2: Selected topics in fuzzy systems designing.- Chapte
r3: Introduction to fuzzy system interpretability.- Chapte
r4: Improving fuzzy systems interpretability by appropriate selection of their structure.- Chapte
r5: Interpretability of fuzzy systems designed in the process of gradient learning.- Chapte
r6: Interpretability of fuzzy systems designed in the process of evolutionary learning.- Chapte
r7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control.- Chapte
r8: Case study: interpretability of fuzzy systems applied to identity verification.- Chapte
r9: Concluding remarks and future perspectives.- Index.
(source: Nielsen Book Data)
This book shows that the term "interpretability" goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics. (source: Nielsen Book Data)