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updated 6 months ago

Biogeography-Based Optimizer (BBO) for training Multi-Layer Perceptron (MLP) by Seyedali Mirjalili

Biogeography-Based Optimizer (BBO) is employed as a trainer for Multi-Layer Perceptron (MLP) (biogeographybased opt..., biogeographybased opt..., bbo)

ACO(ProblemFunction, DisplayFlag)

Ackley

BBO(ProblemFunction, DisplayFlag, ProbFlag, RandSeed)

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updated 7 months ago

Kalman Filter Training for NetLab by Artem Chernodub

Library implements training Neural Network by Kalman Filter algorithm. (extended kalman filte..., multilayer perceptron)

[C,rate]=confmat(Y,T)

bin2classnum(T)

classification_error_bin(T_binary, Y_binary)

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updated 1 year ago

Improved Feedforward Neural Networks Using PSOGSA by Seyedali Mirjalili

This program is an improved Feedforward Neural Network using a hybrid algorithm called PSOGSA. (optimization, particle swarm optimi..., pso)

o=My_FNN(Ino,Hno,Ono,W,B,x1,x2,x3,x4)

output=My_sigmoid(x)

FNNGSA.m

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updated 2 years ago

Machine learning - balancing a stack of balls by Peter Bone

A genetic algorithm is used to train a neural network controller to balance a stack of balls. (machine learning, genetic algorithm, neural network)

LCP(M,q,l,u,x0,display)

MLPControl(u,NN)

crossover(gene1,gene2)

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updated 2 years ago

The matrix implementation of the two-layer Multilayer Perceptron (MLP) neural networks. by Marcelo Fernandes

The matrix implementation of the two-layer Multilayer Perceptron (MLP) neural networks. (ann, mlp, backpropagation)

Y=runMLP(X,Wx,Wy)

[Wx,Wy,MSE]=trainMLP(p,H,m,mu,alpha,X,D,epochMax,MSETarget)

XOR_Example.m

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updated 2 years ago

Multilayer Perceptron Neural Network Model and Backpropagation Algorithm for Simulink by Marcelo Fernandes

Multilayer Perceptron Neural Network Model and Backpropagation Algorithm for Simulink. (ann, mlp, backpropagation)

ANN_MLP_BP_Batch_Mode_Training

ANN_MLP_BP_Incremental_Mode_Training

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updated 2 years ago

Fast multilayer feedforward neural network training by Nicolas

This is a simple and fast code to train neural networks with any number of layers. (neural network, deep network, machine learning)

ask_params(params, field, type, question, value)

bprop(layers, gradient, gradInput, data)

cols(x)

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