Structure based on neural network model of three layer network connection as basis, this paper proposes a new optimization algorithm, this algorithm for the conjugate gradient algorithm to improve the traditional, it is the output weight optimization algorithm based on conjugate gradient algorithm (OWO) and (CG) combining theory is proposed, so we call as the output weight optimization conjugate gradient algorithm (OWO-CG). The new algorithm combines two kinds of algorithm in a body, the whole learning process more quickly and accurately. Algorithm to learn every time the process can be divided into three steps: firstly, according to the error function, convergence factor by using conjugate gradient method, only the changes in input layer and hidden layer weights factor. Then, the output function layer unit calculation, using the output weight optimization output weighting factor theory and solving linear equations in order to get. Finally, the calculation error correcting output function, neural network circuit using the algorithm keeps the value of the difference between the output values and expectations, until meet the accuracy requirements. The experimental results indicate that, compared with the conjugate gradient algorithm and output weight optimization method, this algorithm greatly improves the training speed.
