rbf核神经网络程序matlab程序

1、实用标准文案文档#include RadialBasisNetwork.h#include Exception.h#include Matrix.h#include File.husing namespace std;namespace annie/* Creates a Radial basis function network. All the outputs will have a bias.* param inputs Number of inputs taken in by the network* param centers Number of centers the networ

2、k has. Each center will be* an inputs-dimensional point* param outputs The number of outputs given by the neuron. All of themwill have* a biasreal*/RadialBasisNetwork:RadialBasisNetwork(int inputs, int centers, int outputs,(*CenterArray)1024) : Network(inputs,outputs)int i,j;/extern real CenterArray

3、WORDNUMinputs;centroid = new realinputs;/ Layer of input. Each member is an InputNeuron/InputLayer *_inputLayer;_inputLayer = new InputLayer(0,inputs);/* Number of centers in the network.* If you plan to extend this class, then the onus of keeping this value* consistent lies on you*/_nCenters = cent

4、ers;/ Layer of centers, each member is a CenterNEuron_centerLayer = new Layer(1);for (i=0;i_nCenters;i+)for( j=0;jinputs;j+)centroidj=CenterArrayij;new/CenterNeuron *c = CenterNeuron(Layer:MAX_LAYER_SIZE*1+i,inputs);CenterNeuron *c = new CenterNeuron(Layer:MAX_LAYER_SIZE*1+i,inputs, centroid);for (j

5、=0;jconnect(&_inputLayer-getNeuron(j);_centerLayer-addNeuron(c);/ Layer of output, each member if a SimpleNeuron_outputLayer = new Layer(2);for (i=0;isetActivationFunction(identity,didentity);for (j=0;jconnect(&_centerLayer-getNeuron(j);_outputLayer-addNeuron(n);/ Copy constructor, NOT YET IMPLEMENT

6、EDRadialBasisNetwork:RadialBasisNetwork(RadialBasisNetwork &src) : Network(src) int i,j,lbl;int inputs = src._inputLayer-getSize();int centers = src._centerLayer-getSize();int outputs = src._outputLayer-getSize();new_inputLayer =InputLayer(src._inputLayer-getLabel(),src._inputLayer-getSize();_nCente

7、rs = src._nCenters;lbl = src._centerLayer-getLabel();_centerLayer = new Layer(lbl);for (i=0;igetNeuron(i);实用标准文案c-setCenter(cSrc.getCenter();for (j=0;jconnect(&_inputLayer-getNeuron(j);_centerLayer-addNeuron(c);lbl = src._outputLayer-getLabel();_outputLayer = new Layer(lbl);for (i=0;igetNeuron(i);n-

8、setBias(nSrc.getBias();n-setActivationFunction(identity,didentity);for (j=0;jaddNeuron(n);文档实用标准文案/* Loads a network from a text file* see save* param filename Name of the file from which to load network structure* throws Exception On any error*/RadialBasisNetwork:RadialBasisNetwork(const char *file

9、name) : Network(0,0)File file;int i,j;tryfile.open(filename);catch (Exception &e)string error(getClassName();error = error + : + getClassName() + () - + e.what();throw Exception(error);string s;s=file.readWord();if (pare(getClassName()!=0)string error(getClassName();error = error + : + getClass

10、Name() + () - File supplied is not about this type of network.;throw Exception(error);int maxLayerSize = Layer:MAX_LAYER_SIZE;while (!file.eof()s=file.readWord();if (!pare(INPUTS)_nInputs=file.readInt();_inputLayer = new InputLayer(0,_nInputs);else if (!pare(OUTPUTS)_nOutputs=file.readInt(

11、);_outputLayer = new Layer(2);for (i=0;isetActivationFunction(identity,didentity);_outputLayer-addNeuron(n);else if (!pare(CENTERS)_nCenters = file.readInt();_centerLayer = new Layer(1);for (i=0;iaddNeuron(n);else if (!pare(CENTER_POINTS)for (i=0;igetNeuron(i);VECTOR center;for (j=0;jgetIn

12、putCount();j+)center.push_back(file.readDouble(); n.setCenter(center);else if (!pare(MAX_LAYER_SIZE)maxLayerSize=file.readInt();else if (!pare(Biases)for (i=0;igetNeuron(i);if (file.readChar()=t)o.setBias(file.readDouble();elseo.removeBias();else if (!pare(BEGIN_META_DATA)static const

13、 basic_string :size_type npos = (basic_string :size_type)-1;string end(END_META_DATA);string metaData;s = file.readLine();while (s.find(end,0)=npos)metaData = metaData + s + n;s = file.readLine();if (metaData.length()0)metaData.erase(metaData.length()-1);setMetaData(metaData);else if (!pare(Con

14、nections)/Connect inputs to centersfor (i=0;igetNeuron(i);for (j=0;jgetNeuron(j);/Connect centers to outputsfor (i=0;igetNeuron(i);for (j=0;jgetNeuron(j),file.readDouble();elsecerrgetClassName():getClassName()() - Unrecognizedtoken (sgetNeuron(i).getCenter();catch (Exception &e)string error(getClass

15、Name();error = error + :getCenter() - + e.what();throw Exception(error);return answer;/CenterNeuron&/RadialBasisNetwork:getCenterNeuron(int i)/ try/ / return (CenterNeuron&)(_centerLayer-getNeuron(i);/ / catch (Exception &e)/ /string error(getClassName();/error = error + :getCenterNeuron() - + e.wha

16、t();/throw Exception(error);/ /* Returns the output of the network for the given input.* param input A vector of getDimension() reals* return The corresponding output of the network*/VECTORRadialBasisNetwork:getOutput(VECTOR &input)try_inputLayer-setInput(input);return _outputLayer-getOutput();catch

17、(Exception e)string error(getClassName();error = error + :getOutput() - +e.what();throw Exception(error);/* Sets the ith center point to the given point.* param i The center that is to be changed* param center The getInputCount() dimensional point*/voidRadialBasisNetwork:setCenter(int i, VECTOR &cen

18、ter)tryCenterNeuron &c = (CenterNeuron&)_centerLayer-getNeuron(i);c.setCenter(center);catch (Exception &e)string error(getClassName();error = error + :setCenter() - + e.what();throw Exception(e);/* Sets the ith center point to the given point.* param i The center that is to be changed* param center

19、The getInputCount() dimensional point*/voidRadialBasisNetwork:setCenter(int i, real *center)try文档实用标准文案CenterNeuron &c = (CenterNeuron&)_centerLayer-getNeuron(i); c.setCenter(center);catch (Exception &e)string error(getClassName();error = error + :setCenter() - + e.what();throw Exception(e);/* Sets

20、the weight between the given center and output* param center Index of the center (0=centergetCenterCount().* param output Index of the output (0=outputgetNeuron(center);SimpleNeuron &o = (SimpleNeuron&)_outputLayer-getNeuron(output);o.connect(&c,weight);catch (Exception &e)string error(getClassName(

21、);error = error + :setWeight() - + e.what();throw Exception(error);/* Returns the weight of the link between the given center and output* param center Index of the center (0=centergetCenterCount().* param output Index of the output (0=outputgetNeuron(output);CenterNeuron &c = (CenterNeuron&)_centerL

22、ayer-getNeuron(center); return o.getWeight(&c);catch (Exception &e)string error(getClassName();error = error + :getWeight() - + e.what();throw Exception(error);/* Sets the bias of the ith output.* param i The index of the output (0=igetNeuron(i);n.setBias(bias);catch (Exception &e)string error(getCl

23、assName();error = error + :setBias() - + e.what();throw Exception(e);/* Returns the bias of the ith output* param i The index of the output (0=igetNeuron(i);文档实用标准文案return n.getBias();catch (Exception &e)string error(getClassName();error = error + :setBias() - + e.what();throw Exception(e);/* Wrappe

24、r function to allow getOutput() to work for an array* of real as input as well.* Does exactly the same thing as Network:getOutput(real*). */VECTORRadialBasisNetwork:getOutput(real *input) return Network:getOutput(input); const char * RadialBasisNetwork:getClassName() return RadialBasisNetwork; / The

25、 number of centers in the networkintRadialBasisNetwork:getCenterCount() return _nCenters;/* Sets the activation function of the center neurons.* (The activation function is gaussian by default)* param f The activation function to be used.* param df The derivation of the activation function, used in

26、gradient descent training*/voidRadialBasisNetwork:setCenterActivationFunction(ActivationFunction1 f) /RadialBasisNetwork:setCenterActivationFunction(ActivationFunction f,ActivationFunction df)int i;for (i=0;igetNeuron(i);/c.setActivationFunction(f,df);c.setActivationFunction(f);/* Prevents the ith o

27、utput from having any bias.* param i The index of the output (0=igetNeuron(i);o.removeBias();catch (Exception &e)string error(getClassName();error = error + :removeBias() - + e.what();throw Exception(error);/* Trains the weights of the network, centers are kept fixed.* param T The TrainingSet from w

28、hich input/desired-output pairs will beobtained*/voidRadialBasisNetwork:trainWeights(TrainingSet &T)if (T.getInputSize() != getInputCount()/getInputCount()继承于 Networkstring error(getClassName();error = error + :trainWeights() - Invalid TrainingSet provided.;throw Exception(error);int output;int i,j;

29、int p = T.getSize();/number of training pattersint h = getCenterCount();/number of centersVECTOR in,y;继承于/do for each outputfor (output=0; outputgetNeuron(output);/ 为何要强制类型转换？if (hasBias = outNrn.hasBias()effectiveH+;/setup matricesMatrix *Y = new Matrix(p, 1);/ 存放 p 个模式下输出层各个节点的输出值Matrix *W = NULL;

30、/ 存放权值Matrix *V = new Matrix(p, effectiveH);/ 存放 p 个模式各自隐节点输出值 Matrix *VT = NULL;if (p!=effectiveH)VT = new Matrix(effectiveH ,p);extern int Hidden_num;for (i=0;ip;i+)T.getNextPair(in,y);getOutput(in);for (j=0;jelementAt(i,j) = _centerLayer-getNeuron(j).getOutput();/ 为 第 i,j 个元素赋值if (VT)VT-elementAt

31、(j,i) = V-elementAt(i,j);if (hasBias)V-elementAt(i,j) = 1.0;if (VT)VT-elementAt(j,i) = 1.0;Y-elementAt(i,0) = youtput; / for i=0.p)if (VT)Matrix *VTVinv, *VTY;tryMatrix *VTV;VTV = VT-multiply(V);VTVinv = VTV-inverse();delete VTV;catch (Exception &e)string error(getClassName();error = error + :trainW

32、eights() - + e.what();throw Exception(error);VTY = VT-multiply(Y);W = VTVinv-multiply(VTY);delete VTVinv;delete VTY; / if VTelseMatrix *Vinv;tryVinv = V-inverse();catch (Exception &e)string error(getClassName();error = error + :trainWeights() - + e.what();throw Exception(error);W = Vinv-multiply(Y);

33、delete Vinv;/set the weightsfor (j=0;jelementAt(j,0);if (hasBias)outNrn.setBias(W-elementAt(j,0);/cleanupdelete Y;delete W;delete V;if (VT)delete VT; / for (output = 1.total outputs)/Save the network to a text filevoidRadialBasisNetwork:save(const char *filename)ofstream s;int i,j;s.open(filename,io

34、s:out);if (!s)string error(getClassName();error = error + :save() - Could not open file for writing.;throw Exception(error);sANNIE_FILE ANNIE_VERSIONendl;type offollowings# Network information, the next line identifies the networkendl;s# Constructing a network from a file which doesnt have the ident

35、ifierendl;s# should result in an error endl;s# DO NOT MAKE FORMAT CHANGES TO THIS FILEendl; sRadialBasisNetworkendl;sMAX_LAYER_SIZE Layer:MAX_LAYER_SIZEendl;sINPUTS getInputCount()endl;sCENTERS getCenterCount()endl;sOUTPUTS getOutputCount()endl; snBEGIN_META_DATAendl;if (!getMetaData().empty()sgetMe

36、taData()endl;sEND_META_DATAnendl;sendl;s# Below follows description of the centers. One center per line.endl;s# Each center is a point in d-dimensional space where d = the number of inputsendl;s#文档实用标准文案endl; sCENTER_POINTSendl;for (i=0; igetNeuron(i);VECTOR center = c.getCenter();VECTOR:iterator it

37、;for (it = center.begin(); it!=center.end(); it+)s(*it) ;sendl;sendl;s# Now the biases for each of the outputs. One line per output.endl;has as# Line starts with a t followed by the bias if the neuron biasendl;s# otherwise line has an f indicating no biasendl;s#endl;sBiasesendl;for (i=0;igetNeuron(i);文档实用标准文案if (o.hasBias()st o.getBias();elsesf