Chapter 1 Soft Computing Approach to Pattern Classification and object recognition Abstract The basic aim of this research monograph is to develop a unified approach to supervised pattern classification (Tou and Gonzalez, Pattern Recog nition Principles. Addison-Wesley, Reading, 1974) and model based occluded object recognition(Koch and Kashyap, IEEE Trans Pattern Anal Machine Intell. 9(4): 483-494, 1987; Ray and Dutta Mazumder, Pattern Recogn Lett 9: 351-360 1989). To perform this task we essentially consider soft computing tools, viz uzzy relational calculus(FRC)(Pedrycz, Fuzzy Sets Syst 16: 163-174, 1985 Pattern Recogn 23(112): 121-146, 1990), genetic algorithm (GA)(Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, 1989: Michalewicz, Genetic Algorithm Data Structures Evolution Programs, Springer, New York, 1994)and multilayer perceptron (MLP (Pao, Adaptive pattern recognition and neural networks. Addison Wesle Reading, 1989). The supervised approach to pattern classification and model based approach to occluded object recognition are treated in one framework which is based on either conventional interpretation or new interpretation of multidimen sional fuzzy implication(MFI(Sugeno and Takagi, Fuzzy Sets Syst 9: 313-325 1983; Tsukamoto, Advance in Fuzzy Set Theory and Applications. North-Holland, Amsterdam, 137-149, 1979)and a novel notion of fuzzy pattern vector(FPV. In the context of representation of knowledge about patterns and/or objects we try to generalize the concept of feature vector by fuzzy feature vector. Readers are advised to read Appendix-a before they go into the details of classification (recognition) concept based on soft computing tools. K. S. Ray, Soft Computing Approach to Pattern Classification and Object Recognition, DOI: 10.1007/978-1-4614-5348-2_1 Media New York 2012Chapter 1 Soft Computing Approach to Pattern Classification and Object Recognition Abstract The basic aim of this research monograph is to develop a unified approach to supervised pattern classification (Tou and Gonzalez, Pattern Recog￾nition Principles. Addison-Wesley, Reading, 1974) and model based occluded object recognition (Koch and Kashyap, IEEE Trans Pattern Anal Machine lntell. 9(4):483–494, 1987; Ray and Dutta Mazumder, Pattern Recogn Lett 9:351–360, 1989). To perform this task we essentially consider soft computing tools, viz., fuzzy relational calculus (FRC) (Pedrycz, Fuzzy Sets Syst 16:163–174, 1985, Pattern Recogn 23(1/2):121–146, 1990), genetic algorithm (GA) (Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, 1989; Michalewicz, Genetic Algorithm + Data Structures = Evolution Programs, Springer, New York, 1994) and multilayer perceptron (MLP) (Pao, Adaptive pattern recognition and neural networks. Addison Wesley, Reading, 1989). The supervised approach to pattern classification and model based approach to occluded object recognition are treated in one framework which is based on either conventional interpretation or new interpretation of multidimen￾sional fuzzy implication (MFI) (Sugeno and Takagi, Fuzzy Sets Syst 9:313–325, 1983; Tsukamoto, Advance in Fuzzy Set Theory and Applications. North-Holland, Amsterdam, 137–149, 1979) and a novel notion of fuzzy pattern vector (FPV). In the context of representation of knowledge about patterns and/or objects we try to generalize the concept of feature vector by fuzzy feature vector. Readers are advised to read Appendix-A before they go into the details of classification (recognition) concept based on soft computing tools. K. S. Ray, Soft Computing Approach to Pattern Classification and Object Recognition, DOI: 10.1007/978-1-4614-5348-2_1, Springer Science+Business Media New York 2012 1