This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discrim...

The stability of power production in photovoltaics (PV) power plants is an important issue for large-scale gridconnected systems. This is because it affects the control and operation of the electrical grid. An efficient forecasting model is proposed in this paper to predict the next-day solar photovoltaic power using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and r...

A new algorithm for the solution of large-scale nonlinear complementarity problems is introduced. The algorithm is based on a nonsmooth equation reformulation of the complementarity problem and on an inexact Levenberg-Marquardt-type algorithm for its solution. Under mild assumptions, and requiring only the approximate solution of a linear system at each iteration, the algorithm is shown to be b...

Based on the perspective view of non-linear model fitting, a new algorithm for space resection based on Levenberg-Marquardt algorithm was developed in this paper. The relationship between the new algorithm and the current one, which is commonly implemented in the commercial software, was also discussed. The experimental evaluation of both algorithms with different level of inaccurate initial ap...

Surface wave method is an in-situ nondestructive testing procedure for estimation of elastic moduli and layers thicknesses of layered structures such as pavements and natural soil deposits. In this research "Matlab" has been employed for applying artificial neural networks in solving inversion problem of surface wave test dispersion curve and estimating the soil profile. Multi layer neural netw...

One of the major difficulties facing researchers using neural networks is the selection of the proper size and topology of the networks. The problem is even more complex because often when the neural network is trained to very small errors, it may not respond properly for patterns not used in the training process. A partial solution proposed to this problem is to use the least possible number o...

A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and compare which artificial neural network (ANN) algorithms other intelligence (AI) methods have the most reliable predictor for STS performance. In order gauge performance of STS, research aims implement AI predicting by comparing ANN technique with methods. Three different training are L...

Developing artificial neural network (ANN), a model to make a correct prediction of required force and torque in ring rolling process is developed for the first time. Moreover, an optimal state of process for specific range of input parameters is obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Radii of main roll and mandrel, rotational speed of main roll, pr...

Objective: Soil temperature serves as a key variable in hydrological investigations to determine soil moisture content as well as hydrological balance in watersheds. The ingoing research aims to shed lights on potential of artificial neural networks (ANNs) and Neuro-Fuzzy inference system (ANFIS) to simulate soil temperature at 5-100 cm depths. To satisfy this end, climatic and...