Abstract Various leaning method of neural networks including supervised and unsupervised methods are presented and illustrated with examples. General learning rule as a function of the incoming signals is discussed. Other learning rules such as Hebbian learning, perceptron learning, LMS Least Mean Square learning, delta learning, WTA – Winner Take All learning, and PCA Principal Component Analy...

The Three Step Bacterial Memetic Algorithm is proposed. This new version of the Bacterial Memetic Algorithm with Modified Operator Execution Order (BMAM) is applied in a practical problem, namely is proposed as the Fuzzy Neural Networks (FNN) training algorithm. This paper strove after the improvement of the function approximation capability of the FNNs by applying a combination of evolutionary...

Neural network is one of the new soft computing methods commonly used for prediction of the thermodynamic properties of pure fluids and mixtures. In this study, we have used this soft computing method to predict the coefficients of the Antoine vapor pressure equation. Three transfer functions of tan-sigmoid (tansig), log-sigmoid (logsig), and linear were used to evaluate the performance of diff...

In our previous work we proposed some extensions of the Levenberg-Marquardt algorithm; the Bacterial Memetic Algorithm and the Bacterial Memetic Algorithm with Modified Operator Execution Order for fuzzy rule base extraction from inputoutput data. Furthermore, we have investigated fuzzy flip-flop based feedforward neural networks. In this paper we introduce the adaptation of the Bacterial Memet...

Bundle Adjustment is a common technique to improve results of any multiple view reconstruction algorithm to obtain 3D structure for computer vision and computer graphics. If the error of a reconstruction can be expressed by an error function, this function can be minimized by numerical methods such as the Levenberg-Marquardt algorithm. By this means, the reconstruction can often be significantl...

In this paper, Levenberg-Marquardt (LM) learning algorithm for a single Integrate-and-Fire Neuron (IFN) is proposed and tested for various applications in which a neural network based on multilayer perceptron is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Sever...

In this paper back-propagation artificial neural network (BPANN) with Levenberg–Marquardt algorithm is employed to predict the limiting drawing ratio (LDR) of the deep drawing process. To prepare a training set for BPANN, some finite element simulations were carried out. die and punch radius, die arc radius, friction coefficient, thickness, yield strength of sheet and strain hardening exponent ...

In this paper we develop a convergence analysis in an infinite dimensional setting of the Levenberg–Marquardt iteration for the solution of a hybrid conductivity imaging problem. The problem consists in determining the spatially varying conductivity from a series of measurements of power densities for various voltage inductions. Although this problem has been very well studied in the literature...

A lattice–ladder multilayer perceptron (LLMLP) is an appealing structure for advanced signal processing in a sense that it is nonlinear, possesses infinite impulse response and stability monitoring of it during training is simple. However, even moderate implementation of LLMLP training hinders the fact that a lot of storage and computation power must be allocated. In this paper we deal with the...