Implementation Of Neural Network's Optimal Weights Initialization Technology In Mobile Robot Learning

Based on multiplayer feed-forward neural networks using BP algorithm, considering the fluctuation of initial weights and sample sets, a novel optimal weights initialization technology is proposed for the sake of matching between initial weights and sample sets. In this paper several conclusions are gained:In the condition of adopting multiplayer feed-forward neural network with one output (called single output model below) using BP algorithm, optimal weights initialization technology can gain bigger peak value within several times with great possibility. In the condition of adopting neural network with multiple outputs (called multiple output model below), and in increasing sample dimension and neural network neurons, optimal weights initialization technology can not achieve better initial weights group within several finite steps. As a result, optimal weights initialization technology is suitable to single output model.Analysis on the process of optimal weights initialization technology is achieved and the conclusions made are as follows:The F-norm of weights matrix has little fluctuation when the value of optimal function changes greatly. As to matrixes of different optimal function values, a new matrix, which is the balance between two weights matrix with different optimal function values, is constructed. Analysis on the value of new matrix' s F-norm shows that the difference between two weights matrixes is evident. Optimal weights initialization technology chooses the better weights group for current sample sets.As to the poor performance in achieving big peak value using neural networks with multiple output, a research is made and the conclusions are as follows:To multiple output model, a single network must adapt to all sample sets, and there exists weights' influence in the training process; However in single output model, network group structure is adopted, which conquers the interaction between categories as well as in the weights' change process. To the single output model, the number of variables is several times to multiple output model, and there is no influence between variables. Consequently, the optimal process becomes effortless; To the multiple output model, because of the influence in weights' change process, the appearance possibility of peak value of optimal function is reduced greatly.In this paper, the mobile robot adopts single output model. With the SPCE061A' s voice features, samples are constituted by the encoded output of sensor group and voice teaching. And samples are gained by voice triggering. Robot trains itself with the sample group gathered during samples collection. After the training process, the mobile robot is controlled by the trained neural network directly. In order to observe the difference between the network which adopts optimal weights initialization technology and the one does not, an experiment is done. The result shows that optimal weights initialization technology enhances the speed of convergence greatly.With the inspiration of optimal weights initialization technology, a research on emotion and thinking model is discussed and a solution to balance emotion and thinking is proposed. In order to test the rationality of the loading machine, test software is accomplished. The result shows that the solution to emotion and thinking is rational.