Pengyi Gao

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Although many global optimization search algorithms may be used to train feedforward neural networks, these algorithms have some weaknesses such as dependence of initial solution. This paper proposes a novel hybrid global optimization method for classification problem, called GTA, which combines the advantages of Genetic algorithm and Tabu search. The(More)
The selection for the number of hidden nodes for a neural network is of critical importance. This paper proposes a novel algorithm to determine the number of hidden nodes of a neural network and optimize it. In the method, the number of hidden nodes H is first computed by empirical formulas, and the range of H is determined according to computed result.(More)
When a task is received by an agent organization in multi-agent system (MAS), it should be partitioned into multiple subtasks which will be assigned to suitable agents to be pursued. A goal model of autonomous agent organization (GMAAO) is proposed in which a trigger mechanism of event is added to the traditional “AND/OR” task decomposition tree to deal(More)
In this work, a hybrid neural network model (HNNM) is proposed, which combines the advantages of genetic algorithm, multi-agents and reinforcement learning. In order to generate networks with few connections and high classification performance, HNNM could dynamically prune or add hidden neurons at different stages of the training process. Experimental(More)
As a social entity, agent needs to collaborate with others to execute a task in most multi-agent systems. Agent has its own intention and interest as an individual, which is ignored in most systems and has great influence on cooperating with other agents. In this paper, referring to the principle of interest group in real life, a multi-agent cooperation(More)
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