Leastsquaresbased Multilayer perceptron traning with 基于最小二乘的多层感知器训练.pptVIP

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Leastsquaresbased Multilayer perceptron traning with 基于最小二乘的多层感知器训练.ppt

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Leastsquaresbased Multilayer perceptron traning with 基于最小二乘的多层感知器训练

Least-squares-based Multilayer perceptron training with weighted adaptation -- Software simulation project EE 690 Design of Embodied Intelligence Outline Multilayer Perceptron Least-squares based Learning Algorithm Weighted Adaptation in training Signal-to-Noise Ratio Figure and Overfitting Software simulation project Multilayer perceptron (MLP) Multilayer perceptron (MLP) Efficient mapping from inputs to outputs Powerful universal function approximation Number of inputs and outputs determined by the data Number of hidden neurons Number of hidden layers Multilayer Perceptron Learning Back-propagation (BP) training algorithm: how much each weight is responsible for the error signal BP has two phases: Forward pass phase: feedforward propagation of input signals through network Backward pass phase: propagates the error backwards through network Least-squares based Learning Algorithm Least-squared fit (LSF): to obtain the minimum sum of squared error For underdetermined problem, LSF finds the solution with the minimum SSE For overdetermined problem, pseudo-inverse finds the solution with minimum norm Can be applied in the optimization for weights or signals on the layers Least-squares based Learning Algorithm (I) Least-squares based Learning Algorithm (I) Weights optimization with weighted LSF The location of x on the transfer function determines its effect on output signal of this layer dy/dx ? weighting term in LSF Least-squares based Learning Algorithm (II) II. Weights optimization with iterative fitting W1 can be further adjusted based on the output error Least-squares based Learning Algorithm (III) Least-squares based Learning Algorithm (III) Signal optimization with weighted adaptation The location of x on the transfer function determines how much the signal can be changed Overfitting problem Signal-to-noise ratio figure (SNRF) Sampled data: function value + noise Error signal: approximation error component + noise component Signal-to-noise ratio f