www.gusucode.com > nncontrol 工具箱 matlab 源码程序 > nncontrol/private/nnidenthelp_plant_ident.m
% PLANT IDENTIFICATION % % The Plant Identification process allows you to train a neural network % that models the plant. If the neural network plant model is to be used % in training a controller, you should identify the plant before training % the controller, and you may want to re-identify the plant when controller % training is not satisfactory. % % Plant Identification requires the following parameters: % % 1) Size of the Hidden Layer: Define how many neurons will be in the hidden % layer of the neural network plant model. % 2) Simulink Plant Model: A simulink file, with inport and % outport blocks, used to generate a plant response to train the % neural network plant model. % 3) No. Delayed Controller Outputs: defines how many delays in the controller output % will be used to feed the NN plant model. % 4) No. Delayed Plant Outputs: defines how many delays in the plant output will be % used to feed the NN plant model. % 5) Sampling Interval (in seconds): defines the sampling interval used to collect % data to be used in the training process. % 6) Training function: The training function to be used in the identification % process. % 7.1) Import Training Data: If you select this option, you % enter a valid data file with the input-output values from the % plant to be used for training. % 7.2) Generate Training Data: If you select this option, you % define the range of the input, the limit on the output signal % (if any), and the number of training samples. % 8) Normalize Training Data: If you select this option, the input-output % data is normalized to a range 0-1. % 9) Training Epochs: Defines how many epochs will be used during training. % 10) Use Validation/Testing for Training: If selected, 25 % of the training % data will be used for validation and/or testing. % % The Generate Training Data button generates training data based on the simulink plant % model file (if selected). The input-output data will be displayed % in another window. You can accept or refuse the data. If refused, the % new window is closed and you can adjust parameters on the Plant % Identification window to generate data again. If the data is accepted, you % can then Train the Network. Once the training is concluded you can perform one % of the following actions: % % 1) Generate more data: New training data based on the simulink plant % model file are generated. You can then continue training. % 2) Train Network: The same training data set is used, and the % training continues using the last generated weights. % 3) Apply: The weights are saved in the Neural Network Plant Model block. % You can simulate the system while this window remains open. % 4) OK: The weights are saved in the Neural Network Plant Model block and % the window is closed. % 5) Cancel: The window is closed and no vales are saved. % % During the training process, progress report messages are shown in the % feedback line. % Copyright 1992-2013 The MathWorks, Inc.