Knowledge-Based Neurocomputing: A Fuzzy Logic Approach

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The common denominator of all chapters is the use of knowledge-based methods, in particular ontological means. The chapters are categorized into theory contributions and practical applications.

Knowledge Extraction from Support Vector Machines: A Fuzzy Logic Approach

We hope that this volume will help researchers, students, and practitioners in coping with the challenges of integration, operation, and evaluation of complex systems. Powered by Koha.

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Soft Computing: Fuzzy Logic Approach in Wireless Sensors Networks

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introduction to fuzzy logic part 1 in hindi

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Knowledge-based neurocomputing : a fuzzy logic approach

Authors: Kolman , Eyal, Margaliot , Michael. They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network.


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Conversely, every standard artificial neural network has an equivalent FARB. The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.

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JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Computer Science Artificial Intelligence. Presents the state-of-the-art in knowledge-based neurocomputing Presents a new connection between artificial neural networks ANNs and a special fuzzy-rule base—the all permutations fuzzy-rule base FARB see more benefits. Buy eBook.