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To predict the 100 missing values from a time series of 5000 data points, given for the IJCNN 2004 time series prediction competition, recurrent neural networks (RNNs) are trained with a new learning algorithm. This training algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching(More)
Evaluation of the current board position is critical in computer game engines. In sufficiently complex games, such a task is too difficult for a traditional brute force search to accomplish, even when combined with expert knowledge bases. This motivates the investigation of alternatives. This paper investigates the combination of neural networks, particle(More)
The winner-take-all (WTA) network is useful in database management, very large scale integration (VLSI) design, and digital processing. The synthesis procedure of WTA on single-layer fully connected architecture with sigmoid transfer function is still not fully explored. We discuss the use of simultaneous recurrent networks (SRNs) trained by Kalman filter(More)
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