This paper presents a method to optimally design artificial neural networks with many design parameters using the Design of Experiment (DOE), whose features are efficient experiments using an orthogonal array and quantitative analysis by analysis of variance. Neural networks can approximate arbitrary nonlinear functions. The accuracy of a trained neural network at a certain number of learning cycles depends on both weights and biases and its structure and learning rate. Design methods such as trial-and-error, brute-force approaches, network construction, and pruning, cannot deal with many design parameters such as the number of elements in a layer and a learning rate. Our design method realizes efficient optimization using DOE, and obtains confidence of optimal design through statistical analysis even though trained neural networks very due to randomness in initial weights. We apply our design method three-layer and five-layer feedforward neural networks in a preliminary study and show that approximation accuracy of multilayer neural networks is increased by picking up many more parameters.
This paper describes the development of a high speed dynamic simulator for humanoid robots. In the simulator, an order n formulation is used to solve the inverse dynamics and forward dynamics of a multi-body system. The formulation can deal with a tree structure and multiple contacts with the environment. In order to simulate a collision with friction between the bodies and environments, a virtual spring-damper contact model is proposed. This model enables an accurate computation of the reaction forces and slips. A simulation of ascending steep stairs is carried out in order to demonstrate the validity of the simulation. The results of the simulation are presented and discussed.
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