Behavior Modeling by Neural Networks

Identification of Crack Location and Depth in a Structure by GMDH- type Neural Networks and ANFIS

The Existence of crack in a structure leads to local flexibility and changes  the stiffness and dynamic behavior of the structure. The dynamic behavior of the cracked structure depends on the depth and the location of the crack. Hence, the changes in the dynamic behavior in the structure due to the crack can be used for identifying the location and depth of the crack. In this study the first th...

Neuro-ACT Cognitive Architecture Applications in Modeling Driver’s Steering Behavior in Turns

Cognitive Architectures (CAs) are the core of artificial cognitive systems. A CA is supposed to specify the human brain at a level of abstraction suitable for explaining how it achieves the functions of the mind. Over the years a number of distinct CAs have been proposed by different authors and their limitations and potentials were investigated. These CAs are usually classified as symbolic and...

Artificial neural networks approach for modeling of Cr(VI) adsorption from aqueous solution by MR, MAC, MS

The adsorption ability of Dowex Optipore L493 resin modified with Aliquat 336 (MR), activated carbon modified with Aliquat 336 (MAC) and sawdust modified with Aliquat 336 (MS) for removal of Cr(VI) from aqueous solution in batch system was investigated. The effects of operational parameters such as adsorbent dosage, initial concentration of Cr(VI) ions, pH, temperature and contact time were stu...

Application of multilayer perceptron neural network and support vector machine for modeling the hydrodynamic behavior of permeable breakwaters with porous core

In this research, the application of multilayer perceptron (MLP) neural networks and support vector machine (SVM) for modeling the hydrodynamic behavior of Permeable Breakwaters with Porous Core has been investigated. For this purpose, experimental data have been used on the physical model to relate the reflection and transition coefficients of incident waves as the output parameters to the wid...

Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...

Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)