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This paper presents a non-linear, data driven Adaptive Network based Fuzzy Inference System (ANFIS) modeling of a Two Tanks Hydraulic System (TTHS). The paper also addresses the design of a Type 1 Fuzzy Logic Controller optimized with Genetic Algorithms (GA). The controller was designed and tested in simulation with the obtained ANFIS model and validated in real-time with the actual TTHS. Obtained...
A Deep Neural Network (DNN) using the same activation function for all hidden neurons has an optimization limitation due to its single mathematical functionality. To solve it, a new DNN with different activation functions is designed to globally optimize both parameters (weights and biases) and function selections. In addition, a novel Genetic Deep Neural Network (GDNN) with different activation functions...
In this study, seismic attributes have been used to estimate well logs in one of the Iranian petroleum reservoirs. Three static methods have been evaluated: the linear model, the multilayer perceptron (MLP) and the radial basis function (RBF). For linear case, the selection of appropriate attributes was determined by forward selection and for nonlinear one, the selection was based on the genetic algorithm...
Polynomial Neural Network is a self-organizing network whose performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error. In this paper, a training algorithm for Polynomial Neural Network (PNN) based on Genetic Algorithm (GA) has been proposed for classification problems. A performance comparison of the proposed PNN-GA and Back...
Due to the strong global optimization capability and fast convergence, PSO has shown its efficiency in solving various real world benchmark applications. But premature convergence is one of the major drawback of PSO. In this paper to address this issue, a hybrid PSO-GA based Pi-sigma neural network with standard back propagation gradient descent learning (PSO-GA-PSNN) has been proposed for classification...
In this paper, a novel method for detecting the onset of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is presented. It uses a combination of three different machine learning algorithms in order to get improved results and is based on a three-class classification problem. The three classes for classification considered in this study are normal, very mild AD and mild and moderate...
Considering that the high concentration of mine gas and hydrogen will disturb the output of electrochemical carbon monoxide sensor, this paper integrates gas sensor array with data fusion Algorithm. The output signals of three sensors are trained by BP neural network to get the mathematical model of information fusion for the analysis of mixed gas of methane, hydrogen and carbon monoxide. The experiment...
Considering the issues that the sewage treatment process is a complicated and nonlinear system, it is very difficult to found the process model to describe it, and the key parameters of sewage treatment quality can not be detected on-line, a soft measurement modeling method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic...
Because of the complicated interaction of the sludge compost components, it makes the compost quality evaluation system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost quality evaluation modeling method based on high speed and precise genetic algorithm neural network is presented. The high speed and precise genetic algorithm neural network...
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