By Zdzisław Pawlak (auth.), Prof. Dr. Sankar K. Pal, Prof. Dr. Lech Polkowski, Prof. Dr. Andrzej Skowron (eds.)
Soft computing contains a number of paradigms devoted to nearly fixing real-world difficulties, e.g., in determination making, type or studying; between those paradigms are fuzzy units, tough units, neural networks, and genetic algorithms.
It is easily understood now within the gentle computing neighborhood that hybrid techniques combining numerous paradigms supply very promising makes an attempt to fixing complicated difficulties. Exploiting the capability and energy of either neural networks and tough units, this e-book is dedicated to rough-neurocomputing that is additionally regarding the radical point of computing in accordance with details granulation, particularly to computing with phrases. It offers foundational and methodological concerns in addition to purposes in a number of fields.
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Additional resources for Rough-Neural Computing: Techniques for Computing with Words
Example text
For each test input, the output of an ANN indicates the probability that the test input belongs to the set of measurements associated with the ith life cycle product attribute. Let L denote an output neuron (see Fig. 4) that computes the sum of all weighted outputs from the approximation neurons in the first layer. , reusability, complexity, maintainability [6]) is used to define each life cycle product quality attribute. The application of the rough mereological approach in software quality measurement is described in [38].
Based on the value error Q in (26), adjust the r, w, and u parameters using the usual gradient-based optimization method suggested in (27) and (28): param(new) = param - ad dQ dparam dQ param dQ dy dy aparam . ' (27) (28) A more detailed explanation of how one trains a network containing combinations of AND and OR nodes is given in [31]. 2. Rough-Neural Computing: An Introduction 5 37 Concluding Remarks A scheme for designing rough-neural networks based on an adaptive calculus of granules for distributed systems of cooperating agents has been presented.
The studies of neural networks in the context of rough sets [2,5, 10, 14, 19, 21, 22,24,35-37,41,57,60-63,69,74] and granular computing [12, 31, 40, 44-46, 48,52,53,55,74] are extensive. An intuitive formulation of information granulation was introduced by Zadeh [70, 71]. Practical applications of rough-neural computing have recently been found in predicting urban highway trattic volume l14J, speech analysis [19,24], classifying the waveforms of power system faults [10], signal analysis [36], assessing software quality [31], and control of autonomous vehicles [20].