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In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage...
In this paper, we analyze a class of actor-critic algorithms under partially observable Markov decision process (POMDP) environment. Specifically, in this work we focus on the two-time-scale framework in which the critic uses a temporal difference with neural network (NN) as nonlinear function approximator, and the actor is updated using greedy algorithm with the stochastic gradient approach. Instead...
In this paper, we present the research of a foreign currency investment framework involving the prediction of the foreign currency exchange rates and the portfolio optimization under certain constrains. We adopt two machine learning methods, support vector machines (SVMs) and neural networks (NNs), as well as the traditional moving average method, to predict the exchange rates for three foreign currencies...
This paper presents the research of using bootstrap methods for time-series prediction. Unlike the traditional single model (neural network, support vector machine, or any other types of learning algorithms) based time-series prediction, we propose to use bootstrap methods to construct multiple learning models, and then use a combination function to combine the output of each model for the final predicted...
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