Solving arithmetic problems using feed-forward neural networks

Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...

Solving Fuzzy Equations Using Neural Nets with a New Learning Algorithm

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...

Solving nonlinear Lane-Emden type equations with unsupervised combined artificial neural networks

In this paper we propose a method for solving some well-known classes of Lane-Emden type equations which are nonlinear ordinary differential equations on the semi-innite domain. The proposed approach is based on an Unsupervised Combined Articial Neural Networks (UCANN) method. Firstly, The trial solutions of the differential equations are written in the form of feed-forward neural networks cont...

Using artificial neural networks for solving chemical problems Part I. Multi-layer feed-forward networks

Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement using Feed-Forward and Generalized Regression Neural Networks

Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of ...