Abstract:

This paper proposes a novel algorithm to Optimize the number of Hidden nodes based on Agent(OHA). This approach is completed by two cooperating agents, the RL agent and the NN agent. The RL agent searches better number of hidden nodes according to the reinforcement learning method, and the NN agent optimizes the weights of network with the number by using the separate learning algorithm. After much running, the best solution(weights and hidden nodes) is located. The optimization algorithms and tests are discussed. The test results obtained by using the Iris data set and the risk evaluation data set show the algorithm is better than those by the most commonly used optimization techniques.

Key words: neural networks, hidden node, hiddenlayer architecture optimization, agent, reinforcement learning