In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

To control structures against wind and earthquake excitations, Adaptive Neuro Fuzzy Inference Systems and Neural Networks are combined in this study. The control scheme consists of an ANFIS inverse model of the structure to assess the control force. Considering existing ANFIS controllers, which require a second controller to generate training data, the authors’ approach does not need anot...

Neural receiver structures have been developed for adaptive discrete-signal detection in telecommunication applications. Neural networks combined with conventional equalizers improve the performance especially in compensating for nonlinear distortions. These distortions may result, for instance, from nonlinear ampliication implemented for reducing the power consumption. In this paper, the behav...

the stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. this paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (mdnn) and studies the stability of this algorithm. also, stable learning algorithm for parameters of ...

gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. gas hydrate formation rate is one of the most important topics related to the kinetics of the process of gas hydrate crystallization. the main purpose of this study is investigating phenomenon of gas hyd...

This paper reports on the use of Artificial Neural Networks (ANN) and Partial Least Squareregression (PLS) combined with NIR spectroscopy (900-1700 nm) to design calibration models for thedetermination of sugar content in sugar beet. In this study a total of 80 samples were used as the calibration set,whereas 40 samples were used for prediction. Three pre-processing methods, including Multiplic...

an adaptive version of growing and pruning rbf neural network has been used to predict the system output and implement linear model-based predictive controller (lmpc) and non-linear model-based predictive controller (nmpc) strategies. a radial-basis neural network with growing and pruning capabilities is introduced to carry out on-line model identification.an unscented kalman filter (ukf) algor...

reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. it also decreases memory and transmission bandwidth requirements. moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. in this paper, the kohene...

artificial neural networks (anns) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. however, despite of all advantages cited for artificial neural networks, they have data limitation and need to the large amount of historical data in order to yield accurate results. therefore, the...