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In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis...
In this paper a new method for response integration, based on generalized type-2 fuzzy logic, in modular neural networks (MNNs) is presented. The main idea is that the uncertainty in combining the outputs of the different modules in the MNN can be handled in a better way by using type-2 fuzzy logic. Previous works have considered using interval type-2 fuzzy logic for this task, but in this paper we...
In this study, we investigate the use of a Fuzzy C-Means Clustering based Neural Network (FNN) classifier in problems of emotion classification. The proposed classifier model consists of three layers, namely, input, hidden and output layers. Here, fuzzy c-means clustering method, two types of polynomial and linear combination function are used as a kernel function in the input layer, the hidden layer...
Global Corporations have to manage distributed production over the whole world. Therefore global supply chains are needed. This paper discusses the problem how global production plants and their supply chains can be classified. The classification focuses on demand and supply of production and supply chain. The problem of forecasting the demand of a global supply chain is introduced. The difficulty...
This paper proposes a way to develop a special type of fuzzy logic network—a granular logic network which generalizes the conventional fuzzy logic network by means of expanding the weights (and biases). Information granularity provides some flexibility on the determination of weights of a network. Five protocols are discussed here to realize the granular weights. The optimization of levels of granularities...
Face recognition is a topic of great interest in different areas, especially those related to security. The identification of a person by the image of her face is a difficult task because of changes experienced by the face due to various factors, such as facial expression, aging and even the lighting. This paper presents a new face recognition technique based on the combination of a competitive fuzzy...
This paper describes the optimization of an ensemble neural network with fuzzy integration of responses based on type-1 and type-2 fuzzy logic. Genetic algorithms are used as method of optimization in this case. The time series that is being considered for the ensemble is the US Dollar/MX Peso exchange rate. Simulation results show that the ensemble approach produces good prediction of the exchange...
In this paper a new model of a Hierarchical Genetic Algorithm (HGA) for fuzzy inference system optimization is proposed. The proposed HGA optimizes the fuzzy integrators architecture (type of system, number of trapezoidal membership functions, and their parameters). The model was applied to pattern recognition based on the iris, ear and voice biometrics. Fuzzy logic is used as a method for modular...
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