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An Enhanced Fuzzy Multilayer Perceptron
2004Error back-propagation algorithm of the multilayer perceptron may result in local-minima because of the insufficient nodes in the hidden layer, inadequate momentum set-up, and initial weights. In this paper, we proposed the fuzzy multilayer perceptron which is composed of the ART1 and the fuzzy neural network.
Kwang-Baek Kim, Choong Shik Park
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Exponential-weight multilayer perceptron
2017 International Joint Conference on Neural Networks (IJCNN), 2017Analog integrated circuits may increase the neuromorphic network performance dramatically, leaving far behind their digital and biological counterparts, while approaching the energy efficiency of the brain. The key component of the most advanced analog circuit implementations is a nanodevice with adjustable conductance — essentially an analog ...
Farnood Merrikh-Bayat +2 more
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An Approach to Encode Multilayer Perceptrons
2002Genetic connectionism is based on the integration of evolution and neural network learning within one system. An overview of the Multilayer Perceptron encoding schemes is presented. A new approach is shown and tested on various case studies. The proposed genetic search not only optimizes the network topology but shortens the training time.
Jerzy Korczak 0001, Emmanuel Blindauer
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Interactive initialization of the multilayer perceptron
Pattern Recognition Letters, 2000Abstract A new multilayer preceptor initialization method is proposed and compared experimentally with a traditional random initialization method. An operator maps training-set vectors into a two-variate space, inspects bi-variate training-set vectors and controls the complexity of the decision boundary.
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Multilayer perceptrons for classification and regression
Neurocomputing, 1991Abstract We review the theory and practice of the multilayer perceptron. We aim at addressing a range of issues which are important from the point of view of applying this approach to practical problems. A number of examples are given, illustrating how the multilayer perceptron compares to alternative, conventional approaches.
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Fast training of multilayer perceptrons
IEEE Transactions on Neural Networks, 1997Training a multilayer perceptron by an error backpropagation algorithm is slow and uncertain. This paper describes a new approach which is much faster and certain than error backpropagation. The proposed approach is based on combined iterative and direct solution methods. In this approach, we use an inverse transformation for linearization of nonlinear
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Multilayer Perceptron for Label Ranking
2012Label Ranking problems are receiving increasing attention in machine learning. The goal is to predict not just a single value from a finite set of labels, but rather the permutation of that set that applies to a new example (e.g., the ranking of a set of financial analysts in terms of the quality of their recommendations).
Geraldina Ribeiro +3 more
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Water Quality Prediction Using KNN Imputer and Multilayer Perceptron
Water (Switzerland), 2022Muhammad Umer +2 more
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