Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water log...

The present study aims to utilize an Artificial Neural Network (ANN) to modeling the rainfallrunoff relationship in a catchment area located in a semiarid region of Iran. The paper illustrates the applications of the feed forward back propagation for the rainfall forecasting with various algorithms with performance of multi-layer perceptions. The monthly stream of Jarahi Watershed was analyzed ...

Artificial neural networks (ANN) is an approach to solving different tasks. In this paper we forecast U.S. stock market movements using two types of artificial neural networks: a network based on the Levenberg-Marquardt learning mechanism and a synergetic network which was described by German scientist Herman Haken. The LevenbergMarquardt ANN is widely used for forecasting financial markets, wh...

In this research samples of PVOH were synthesized at various reaction conditions (temperature, time, and amount of catalyst). First at 25˚C and 45˚C and constant catalyst weight samples of PVOH were prepared with different degree of hydrolysis at various times. For investigation of the effects of temperature, at times 20 and 40 min and constant weight of catalyst PVOH was prepared at various te...

creep compliance is one of the fundamental tests of mechanistic- empirical flexible pavement design procedure in the aashto 2002 design guide. in this research, a new artificial neural network model for estimating the hma creep compliance with the generalization ability of r=0.949 has been developed successfully using feed forward multi layer perceptron artificial neural networks (anns) with le...

This work describes a methodology that combines logic-based systems and connectionist systems. Our approach uses finite truth-valued Lukasiewicz logic, wherein every connective can be defined by a neuron in an artificial network [1]. This allowed the injection of first-order formulas into a network architecture, and also simplified symbolic rule extraction. For that we trained a neural networks...

Genetic algorithm and partial least square (GA-PLS), the kernel PLS (KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlationbetween retention time (RT) and descriptors for 15 nanoparticle compounds which obtained by thecomprehensive two dimensional gas chromatography system (GC x GC). Application of thedodecanethiol monolayer-protect...

Singularities and uncertainty in manipulator dynamic is a major issue in kinematic control of manipulator which is obtained by applying robot model. In this paper, artificial neural networks with optimal training process and training data have been proposed as a way to solve this problem. The main idea of this approach is to use an artificial neural network to learn the characteristics of the r...

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algor...