matlab BP神经网络 newff函数官方源码

最近在学习BP神经网络，想要自定义神经网络内部，但是网上找了很久都没有找到源代码，于是自己去搬运了一波matlab官方的源代码。

不多说，直接上代码，未编辑过的源码：

function out1 = newff(varargin)
%NEWFF Create a feed-forward backpropagation network.
%
%  Obsoleted in R2010b NNET 7.0.  Last used in R2010a NNET 6.0.4.
%  The recommended function is <a href="matlab:doc feedforwardnet">feedforwardnet</a>.
%
%  Syntax
%
%    net = newff(P,T,S)
%    net = newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF)
%
%  Description
%
%    NEWFF(P,T,S) takes,
%      P  - RxQ1 matrix of Q1 representative R-element input vectors.
%      T  - SNxQ2 matrix of Q2 representative SN-element target vectors.
%      Si  - Sizes of N-1 hidden layers, S1 to S(N-1), default = [].
%            (Output layer size SN is determined from T.)
%    and returns an N layer feed-forward backprop network.
%
%    NEWFF(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DDF) takes optional inputs,
%      TFi - Transfer function of ith layer. Default is 'tansig' for
%            hidden layers, and 'purelin' for output layer.
%      BTF - Backprop network training function, default = 'trainlm'.
%      BLF - Backprop weight/bias learning function, default = 'learngdm'.
%      PF  - Performance function, default = 'mse'.
%      IPF - Row cell array of input processing functions.
%            Default is {'fixunknowns','remconstantrows','mapminmax'}.
%      OPF - Row cell array of output processing functions.
%            Default is {'remconstantrows','mapminmax'}.
%      DDF - Data division function, default = 'dividerand';
%    and returns an N layer feed-forward backprop network.
%
%    The transfer functions TF{i} can be any differentiable transfer
%    function such as TANSIG, LOGSIG, or PURELIN.
%
%    The training function BTF can be any of the backprop training
%    functions such as TRAINLM, TRAINBFG, TRAINRP, TRAINGD, etc.
%
%    *WARNING*: TRAINLM is the default training function because it
%    is very fast, but it requires a lot of memory to run.  If you get
%    an "out-of-memory" error when training try doing one of these:
%
%    (1) Slow TRAINLM training, but reduce memory requirements, by
%        setting NET.<a href="matlab:doc nnproperty.net_efficiency">efficiency</a>.<a href="matlab:doc nnproperty.net_efficiency_memoryReduction">memoryReduction</a> to 2 or more. (See HELP TRAINLM.)
%    (2) Use TRAINBFG, which is slower but more memory efficient than TRAINLM.
%    (3) Use TRAINRP which is slower but more memory efficient than TRAINBFG.
%
%    The learning function BLF can be either of the backpropagation
%    learning functions such as LEARNGD, or LEARNGDM.
%
%    The performance function can be any of the differentiable performance
%    functions such as MSE or MSEREG.
%
%  Examples
%
%    [inputs,targets] = simplefitdata;
%    net = newff(inputs,targets,20);
%    net = train(net,inputs,targets);
%    outputs = net(inputs);
%    errors = outputs - targets;
%    perf = perform(net,outputs,targets)
%
%  Algorithm
%
%    Feed-forward networks consist of Nl layers using the DOTPROD
%    weight function, NETSUM net input function, and the specified
%    transfer functions.
%
%    The first layer has weights coming from the input.  Each subsequent
%    layer has a weight coming from the previous layer.  All layers
%    have biases.  The last layer is the network output.
%
%    Each layer's weights and biases are initialized with INITNW.
%
%    Adaption is done with TRAINS which updates weights with the
%    specified learning function. Training is done with the specified
%    training function. Performance is measured according to the specified
%    performance function.
%
%  See also NEWCF, NEWELM, SIM, INIT, ADAPT, TRAIN, TRAINS% Mark Beale, 11-31-97
% Copyright 1992-2010 The MathWorks, Inc.%disp('NEWFF is no longer recommended. FEEDFORWARD is simpler and more efficient.');
% TODO - Recommendation function NNRECOMMEND%% Boilerplate Code - Same for all Network Functionspersistent INFO;
if (nargin < 1), error(message('nnet:Args:NotEnough')); end
in1 = varargin{1};
if ischar(in1)switch in1case 'info',if isempty(INFO), INFO = get_info; endout1 = INFO;end
elseout1 = create_network(varargin{:});
end%% Boilerplate Code - Same for all Network Functions%%
function info = get_infoinfo.function = mfilename;
info.name = 'Feed-Forward';
info.description = nnfcn.get_mhelp_title(mfilename);
info.type = 'nntype.network_fcn';
info.version = 6.0;%%
function net = create_network(varargin)if nargin < 2, error(message('nnet:Args:NotEnough')), endv1 = varargin{1};
if isa(v1,'cell'), v1 = cell2mat(v1); end
v2 = varargin{2};
if nargin > 2, v3 = varargin{3}; endif (nargin<= 6) && (size(v1,2)==2) && (~iscell(v2)) && (size(v2,1)==1) && ((nargin<3)||iscell(v3))nnerr.obs_use(mfilename,['See help for ' upper(mfilename) ' to update calls to the new argument list.']);net = new_5p0(varargin{:});
elsenet = new_5p1(varargin{:});
end%=============================================================
function net = new_5p1(p,t,s,tf,btf,blf,pf,ipf,tpf,ddf)if nargin < 2, error(message('nnet:Args:NotEnough')), end% Defaults
if (nargin < 3), s = []; end
if (nargin < 4), tf = {}; end
if (nargin < 5), btf = 'trainlm'; end
if (nargin < 6), blf = 'learngdm'; end
if (nargin < 7), pf = 'mse'; end
if (nargin < 8), ipf = {'fixunknowns','removeconstantrows','mapminmax'}; end
if (nargin < 9), tpf = {'removeconstantrows','mapminmax'}; end
if (nargin < 10), ddf = 'dividerand'; end% Format
if isa(p,'cell'), p = cell2mat(p); end
if isa(t,'cell'), t = cell2mat(t); end% Error checking
if ~(isa(p,'double') || isreal(p)  || islogical(t))error(message('nnet:NNet:XNotLegal'))
end
if ~(isa(t,'double') || isreal(t) || islogical(t))error(message('nnet:NNet:TNotLegal'))
end
if isa(s,'cell')if (size(s,1) ~= 1)error(message('nnet:NNet:LayerSizes'))endfor i=1:length(s)si = s{i};if ~isa(si,'double') || ~isreal(si) || any(size(si) ~= 1) || any(si<1) || any(round(si) ~= si)error(message('nnet:NNet:LayerSizes'))endends = cell2mat(s);
end
if (~isa(s,'double')) || ~isreal(s) || (size(s,1) > 1) || any(s<1) || any(round(s) ~= s)error(message('nnet:NNet:LayerSizes'))
end% Architecture
Nl = length(s)+1;
net = network;
net.numInputs = 1;
net.numLayers = Nl;
net.biasConnect = ones(Nl,1);
net.inputConnect(1,1) = 1;
[j,i] = meshgrid(1:Nl,1:Nl);
net.layerConnect = (j == (i-1));
net.outputConnect(Nl) = 1;% Simulation
net.inputs{1}.processFcns = ipf;
for i=1:Nlif (i < Nl)net.layers{i}.size = s(i);if (Nl == 2)net.layers{i}.name = 'Hidden Layer';elsenet.layers{i}.name = ['Hidden Layer ' num2str(i)];endelsenet.layers{i}.name = 'Output Layer';endif (length(tf) < i) || all(isnan(tf{i}))if (i<Nl)net.layers{i}.transferFcn = 'tansig';elsenet.layers{i}.transferFcn = 'purelin';endelsenet.layers{i}.transferFcn = tf{i};end
end
net.outputs{Nl}.processFcns = tpf;% Adaption
net.adaptfcn = 'adaptwb';
net.inputWeights{1,1}.learnFcn = blf;
for i=1:Nlnet.biases{i}.learnFcn = blf;net.layerWeights{i,:}.learnFcn = blf;
end% Training
net.trainfcn = btf;
net.dividefcn = ddf;
net.performFcn = pf;% Initialization
net.initFcn = 'initlay';
for i=1:Nlnet.layers{i}.initFcn = 'initnw';
end% Configuration
% Warning: Use of these properties is no longer recommended
net.inputs{1}.exampleInput = p;
net.outputs{Nl}.exampleOutput = t;% Initialize
net = init(net);% Plots
net.plotFcns = {'plotperform','plottrainstate','plotregression'};%================================================================
function net = new_5p0(p,s,tf,btf,blf,pf)
% Backward compatible to NNT 5.0if nargin < 2, error(message('nnet:Args:NotEnough')), end% Defaults
Nl = length(s);
if nargin < 3, tf = {'tansig'}; tf = tf(ones(1,Nl)); end
if nargin < 4, btf = 'trainlm'; end
if nargin < 5, blf = 'learngdm'; end
if nargin < 6, pf = 'mse'; end% Error checking
if isa(p,'cell') && all(size(p)==[1 1]), p = p{1,1}; end
if (~isa(p,'double')) || ~isreal(p)error(message('nnet:NNData:XNotMatorCell1Mat'))
end
if isa(s,'cell')if (size(s,1) ~= 1)error(message('nnet:NNet:LayerSizes'))endfor i=1:length(s)si = s{i};if ~isa(si,'double') || ~isreal(si) || any(size(si) ~= 1) || any(si<1) || any(round(si) ~= si)error(message('nnet:NNet:LayerSizes'))endends = cell2mat(s);
end
if (~isa(s,'double')) || ~isreal(s) || (size(s,1) ~= 1) || any(s<1) || any(round(s) ~= s)error(message('nnet:NNet:LayerSizes'))
end% Architecture
net = network(1,Nl);
net.biasConnect = ones(Nl,1);
net.inputConnect(1,1) = 1;
[j,i] = meshgrid(1:Nl,1:Nl);
net.layerConnect = (j == (i-1));
net.outputConnect(Nl) = 1;% Simulation
for i=1:Nlnet.layers{i}.size = s(i);net.layers{i}.transferFcn = tf{i};
end% Performance
net.performFcn = pf;% Adaption
net.adaptfcn = 'adaptwb';
net.inputWeights{1,1}.learnFcn = blf;
for i=1:Nlnet.biases{i}.learnFcn = blf;net.layerWeights{i,:}.learnFcn = blf;
end% Training
net.trainfcn = btf;% Initialization
net.initFcn = 'initlay';
for i=1:Nlnet.layers{i}.initFcn = 'initnw';
end% Warning: this property is no longer recommended for use
net.inputs{1}.exampleInput = p;net = init(net);% Plots
net.plotFcns = {'plotperform','plottrainstate','plotregression'};

matlab BP神经网络 newff函数官方源码相关推荐

matlab BP神经网络 newff
net = newff(P,T,S) % 这两种定义都可以 net = newff(P,T,S,TF,BTF,BLF,PF,IPF,OPF,DD ...
【电力负荷预测】基于matlab BP神经网络电力负荷预测【含Matlab源码 278期】
⛄一.获取代码方式获取代码方式1: 完整代码已上传我的资源: [电力负荷预测]基于matlab BP神经网络电力负荷预测[含Matlab源码 278期] 获取代码方式2: 付费专栏Matlab智能算 ...
【停车位预测】基于matlab BP神经网络停车位预测【含Matlab源码 765期】
⛄一.获取代码方式获取代码方式1: 完整代码已上传我的资源:[停车位预测]基于matlab BP神经网络停车位预测[含Matlab源码 765期] 点击上面蓝色字体,直接付费下载,即可. 获取代码方 ...
【故障诊断分析】基于matlab BP神经网络三相逆变器故障诊断研究【含Matlab源码 1736期】
一.BP神经网络三相逆变器故障诊断简介针对三相桥式逆变电路为研究对象,建立了仿真模型,并对逆变器主电路开关器件的开路故障进行仿真,提出了基于BP神经网络的故障诊断方法,确定了网络的结构和参数,并以 ...
matlab神经网络newff函数的用法
转自:matlab神经网络newff函数的用法,保存在此以学习. 设[P,T]是训练样本,[X,Y]是测试样本: net=newrb(P,T,err_goal,spread); %建立网络 q=sim ...
matlab里newff,新版matlab中神经网络训练函数newff的使用方法
新版matlab中神经网络训练函数newff的使用方法新版 Matlab 中神经网络训练函数 Newff 的使用方法一. 介绍新版 newffSyntax net = newff(P,T,[S1 ...
新版matlab newff,[转载]新版Matlab中神经网络训练函数Newff的使用方法
新版Matlab中神经网络训练函数Newff的使用方法一. 介绍新版newff Syntax · net = newff(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, ...
Matlab newff 训练时间,新版Matlab中神经网络训练函数Newff的使用方法
新版Matlab中神经网络训练函数Newff的使用方法一. 介绍新版newff Syntax · net = newff(P,T,[S1 S2...S(N-l)],{TF1 TF2...TFNl}, ...
《MATLAB 神经网络43个案例分析》：第3章遗传算法优化BP神经网络——非线性函数拟合
<MATLAB 神经网络43个案例分析>:第3章遗传算法优化BP神经网络--非线性函数拟合 1. 前言 2. MATLAB 仿真示例 3. 小结 1. 前言 <MATLAB 神经网 ...

matlab BP神经网络 newff函数官方源码

matlab BP神经网络 newff函数官方源码相关推荐

最新文章

热门文章