I am using R2010a, network is as follows, it is a elman neural network, trained with 'trainrp' and nguyen widrow algorithm is used to generate initial bias values and weights val =
Neural Network object: architecture: numInputs: 1 numLayers: 10 biasConnect: [1; 1; 1; 1; 1; 1; 1; 1; 1; 1] inputConnect: [1; 0; 0; 0; 0; 0; 0; 0; 0; 0] layerConnect: [10x10 boolean] outputConnect: [0 0 0 0 0 0 0 0 0 1] numOutputs: 1 (read-only) numInputDelays: 0 (read-only) numLayerDelays: 1 (read-only) subobject structures: inputs: {1x1 cell} of inputs layers: {10x1 cell} of layers outputs: {1x10 cell} containing 1 output biases: {10x1 cell} containing 10 biases inputWeights: {10x1 cell} containing 1 input weight layerWeights: {10x10 cell} containing 18 layer weights functions: adaptFcn: 'learngd' divideFcn: 'dividenull' gradientFcn: 'calcgbtt' initFcn: 'initlay' performFcn: 'mse' plotFcns: {'plotperform','plottrainstate'} trainFcn: 'trainrp' parameters: adaptParam: .lr divideParam: (none) gradientParam: (none) initParam: (none) performParam: (none) trainParam: .show, .showWindow, .showCommandLine, .epochs, .time, .goal, .max_fail, .min_grad, .delt_inc, .delt_dec, .delta0, .deltamax weight and bias values: IW: {10x1 cell} containing 1 input weight matrix LW: {10x10 cell} containing 18 layer weight matrices b: {10x1 cell} containing 10 bias vectors other: name: '' userdata: (user information)
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