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Traditional method of tender offer is subjective and arbitrary, and the ARIMA Accuracy can't satisfy the tenderer. We have combined the Elman NN with the SVM model to establish a new hybrid optimization algorithm, which are presented to the bidding tender offer in a project. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature...
Artificial Neural Networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This paper presents the development of a simple ANN topology for load forecasting model with much improved accuracy for the Regional Power Control Centre of Saudi Electricity Company. The proposed system is based on optimising the initial random...
Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual...
This paper proposes a new approach to solve Traveling Salesman Problems (TSPs) by using a class of Lotka-Volterra neural networks (LVNN) without self-excitatory. Some stability criteria that ensure the convergence of valid solutions are obtained. It is proved that a class of equilibrium states are stable if and only if they correspond to the valid solutions of the TSPs. That is, one can always obtain...
The use of recurrent neural networks in bioprocess identification and optimization is investigated. A recurrent neural network is trained on a set of fermentation data, and there-after used as a nonlinear process model to estimate nonmeasurable process states at different conditions. With the bioprocess state variable information available, an optimization technique can be used to generate optimum...
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