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This paper briefly discusses the basic principle of artificial neural network. BP network model based on time series has been established through an instance. Training and testing have been done for the network using existing observation data. Compared with the measured value through regression analysis, the effectiveness and accuracy of the network have been proved. It can be a prediction method...
Feed-forward neural networks has been used in many areas, but still with limited generalization and slow convergence. This research uses the simple form of the feed-forward neural networks, the multi-layer perceptrons, continuing the other previous research that use inverse function of the activation function with weight ratio, to cut down the execution time from days into minutes in a learning system,...
Using the adequate number of Multilayer Perceptron input, hidden and output layers, the Cellular Neural Network dynamic behavior, when the system converges to a fixed-point, can be reproduced by a Multilayer Perceptron with restrictions. A Multilayer Perceptron can then be defined in order to act as a two neuron Cellular Neural Network and vice-versa. From this, we combine their properties in order...
Many interesting problems in reinforcement learning (RL) are continuous and/or high dimensional, and in this instance, RL techniques require the use of function approximators for learning value functions and policies. Often, local linear models have been preferred over distributed nonlinear models for function approximation in RL. We suggest that one reason for the difficulties encountered when using...
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