Mahesh Pudari Defining the network topology, initializing the network parameters, and repeatedly modifying the parameters to minimize the difference between the expected and actual output are all processes in training a pi-sigma neural network (PSNN).
In MATLAB, here's an example of how to train a PSNN:
% Define the network architecture
numInputs = 2;
numHidden = 2;
numOutputs = 1;
% Initialize network parameters
weights1 = rand(numInputs, numHidden);
weights2 = rand(numHidden, numOutputs);
% Define the training data
inputs = [1 2; 3 4; 5 6];
targets = [0.5; 0.7; 0.9];
% Define the learning rate
learningRate = 0.1;
% Define the number of iterations
numIterations = 1000;
% Train the network
for i = 1:numIterations
% Forward propagation
hidden = pi(inputs * weights1);
outputs = sigma(hidden * weights2);
% Backpropagation
errors = targets - outputs;
delta2 = errors .* sigma(outputs, true);
delta1 = delta2 * weights2' .* pi(hidden, true);
% Update weights
weights2 = weights2 + hidden' * delta2 * learningRate
In the preceding code sample, we first establish the PSNN's architecture. The network in this scenario contains two inputs, two hidden neurons, and one output. The network parameters, weights1 and weights2, are then initialized. Following that, we define the training data inputs and targets. Then we specify a learning rate and the number of training iterations. In the for loop, we do forward and backpropagation and update the weights accordingly.
It's worth noting that the pi and sigma functions in the above example represent the PSNN's activation functions. These functionalities can be customized to meet your needs. It's also worth noting that this is a simplified example, and it's not guaranteed to work in all circumstances; the architecture, learning rate, and number of iterations may need to be tweaked based on the individual problem at hand.
To avoid overfitting during the training phase, you may want to consider adding regularization such as L2 or dropout.
Mahesh Pudari A Pi-Sigma neural network is a type of recurrent neural network (RNN) that is used for time series prediction and control. It is composed of two parts: a Pi-neuron, which is responsible for predicting the next value in the time series, and a Sigma-neuron, which is responsible for generating the control signal.
Mahesh Pudari Here is an example of Matlab code for training a Pi-Sigma neural network:
% Define network architecture
numInputNeurons = 1;
numPiNeurons = 10;
numSigmaNeurons = 1;
net = newpisigma(numInputNeurons,numPiNeurons,numSigmaNeurons);
% Prepare training data
inputData = ... % Input time series data
targetData = ... % Target output data for the time series
% Train network
[net,tr] = train(net,inputData,targetData);
Mahesh Pudari In this example, numInputNeurons is the number of input neurons, numPiNeurons is the number of Pi-neurons, and numSigmaNeurons is the number of Sigma-neurons. The newpisigma function creates a new Pi-Sigma neural network with the specified architecture, and the train function trains the network using the input and target data.
using MATLAB see this
https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html