Approximation theory and feedforward networks
Neural Networks
On the Problem of Local Minima in Backpropagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the convergence of the back-propagation algorithm
Neural Networks
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motivating students to engage in experiential learning: a tension-to-learn theory
Simulation and Gaming
Elements of Forecasting
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm
IEEE Transactions on Neural Networks
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Magnified gradient function with deterministic weight modification in adaptive learning
IEEE Transactions on Neural Networks
A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients
IEEE Transactions on Neural Networks
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This paper proposes a novel approach, namely, the Back-propagation with diversive curiosity (DCPROP) algorithm, for solving the ''flat spot'' problem and for escaping from local minima. Representing the diversive curiosity, an internal indicator is designed for BP algorithm, which detects the phenomenon of being trapped in local minima and the occurrence of premature convergence. Upon such detection, the neural network is activated again to explore optimal solution in search space and escape form local minima by means of stochastic disturbance. The proposed DCPROP algorithm is implemented and applied to two well-known face recognition problems, and the results are compared with Standard Back-propagation (SBP).