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Neo-Fuzzy Supported Brain Emotional Learning Based Pattern Recognizer for Classification Problems


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Abstract


Based on the limbic system theory of mammalian emotional brain, supervised Brain Emotional Learning Based Pattern Recognizer (BELPR) has been recently proposed for multi-input, multi-output classification problems. It offers features like: decreased time and spatial complexity, faster training and higher accuracy. BELPR has been deployed to classify a number of benchmark datasets and has demonstrated its superior performance compared to the conventional Multilayer Perceptron (MLP) network. The goal of this study is to further enhance the classification accuracy of BELPR through integration with Neo-Fuzzy Neurons (NFN). The network built using NFN shares many of the same characteristics as BELPR, such as: simplicity, transparency, accuracy and lower computational complexity. With this view in mind, the study proposes a hybrid classification network: Neo-Fuzzy Supported Brain Emotional Learning Based Pattern Recognizer (NFBELPR) which will preserve the features of both networks, while simultaneously improving the performance of BELPR. The NFBELPR model can be considered as a group of two networks depending upon the level of integration of NFN and BELPR. When the integration of NFN is only considered in the orbitofrontal cortex (OFC) section of BELPR, the resulting classification model is termed as Partially Integrated NFBELPR (PNFBELPR). In cases when the integration is considered both in the OFC and amygdala (AMY) sections of BELPR, the resulting classification model becomes Fully Integrated NFBELPR (FNFBELPR). The proposed NFBELPR networks are implemented in MATLABĀ® R2009b programming environment to classify a number of benchmark datasets. They are found to achieve higher classification accuracy when compared to BELPR and some state of the art classification networks.

KeyWords
Brain emotional learning, neural networks, neofuzzy neuron, classification, MATLAB



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