A recognition system based on fuzzy set theory and approximate reasoning has been described that is capable of handling various imprecise input patterns and providing a natural decision system. The input feature is considered to be of either quantative form or linguistic form or mixed form or set form. The entire feature space is decomposed here into some overlapping subdomains depending on the geometric structure and the relative position of the pattern classes found in the training samples. The various uncertainty (ambiguity) in the input statement has been managed by providinglmodifying membership values to a great extent. A relational matrix corresponding to the subdomains and the pattern classes has been considered in the modified Zadeh's compositional rule of inference in order to recognize the samples. The linguistic output decision is associated with a confidence factor denoting the degree of certainty of a decision. The effectiveness of the algorithm has been demonstrated on some artificially generated patterns and also on the real life speech data. The recognition scores are described in terms of various choices namely, single correct, first correct, combined correct, second correct and fully wrong choices; thus provides a low rate of misclassification as compared to the conventional two-state systems.
Approximate Reasoning is the process Ill" processes by which a possible imprecise conclusion is deduced from a collection of imprecise premises. Fuzzy logic plays the major role in approximate reasoning. It has the ability to deal with different types of uncertainty.An overview of the different aspects of the theory of appro:x:imate reasoning has been provided here based on the e:x:isting literature. Suitable iUustratioDB are included, whenever necessary, to make the concept clear. Some of the implementation of the theory to real life problems have been mentioned. Finally, a linguistic re cognition system based on approximate reasoning has heen described along with its implementation in bpeed1 recognition problem. Indexing terms; Fuzzy sets, Approximate reasoning, Management of uncertainty, Recognition systemLOGIC, according to Webster's dictionary, is the science combination of predicate logic and probabilitY-based of the formal principles of reasoning. In this sense, methods. A serious short coming of these methods is fuzzy logic is concerned with the formal principles of that they are not capable of handling pervisive fuzziness approximate reasoning. To be more specific, it aims at of information in the knowledge base and, as a result, modelling the human reasoning system. Most of human are mostly ad hoc in nature. reasoning is approximate rather than precise in nature.Zadeh has suggested an alternative approach [2] to Based On a store of knowledge, we have the ability to the management of uncertainty which is based on fuzzy infer an approximate answer to a question. For example:logic. A feature of fuzzy logic which is of particular (i) Ram is much younger than Madhu. What is importance to the management of uncertainty in know the age of Ram? ledge based system is that it provides a systematic frame work for dealing with fuzzy quantifiers eg, most, many, (ii) Smartness is attractive. raga is smart. Is raga about, few etc. In this way, fuzzy logic subsumes both attractive? predicate logic and probability theory, and makes it Fuzzy logic addresses these problems in the following possible to deal with different types of uncertainty within ways. First, the meaning of an imprecise proposition is a single conceptual frame-work. represented as an elastic constraint on a variable; and During the past few years fuzzy logic has found several second, the answer to a query is deduced through a propa applications ranging from process control to medical gation of elastic constraints.diagnosis. The basic idea underlying fuzzy logic was By approximate reasoning, we mean a type of reason suggested by Zadeh [3][4][5]. Mamdadani and Assilian ing, which is neither very exact nor very inexact. In found its first application [6] in connection with the regu~ other words, it is the process or processes by which a latiou of a steam engine. One of the first commercial possible imprecise conclusion is deduced from a collection applications of fuzzy logic to process control was the of imprecise premises. In a simplest way, we can say de...
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