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In this study, a fuzzy sliding mode controller for a rotary inverted pendulum is designed. Sliding mode controllers are high performance nonlinear controllers. Not only Sliding mode controller stabilizes the system under control effectively but also it robustly compensates the effect of bounded uncertainties and shows invariance properties in the presence of bounded disturbance. In this study, a fuzzy...
This study addresses new hybrid approaches for velocity control of an electro hydraulic servosystem (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we combined classical method based-on sliding mode control and fuzzy RBF networks. The control by using adaptive networks need plant's Jacobean, but here this problem solved by sliding surface. It is demonstrated...
Premature ventricular contraction (PVC) beats are of great importance in evaluating and predicting life threatening ventricular arrhythmias. The aim of this study is to improve the diagnosis level of detection of PVC arrhythmia from ECG signals. This improvement is based on an appropriate choice of features for the selected task. We extracted fourteen features including, temporal, morphological features...
In this study, a hybrid learning algorithm for training the recurrent fuzzy neural network (RFNN) is introduced. This learning algorithm aims to solve main problems of the gradient descent (GD) based methods for the optimization of the RFNNs, which are instability, local minima and the problem of generalization of trained network to the test data. PSO as a global optimizer is used to optimize the...
Industrial robots are important in production processes especially automotive industry for increase rate of production, products quality promotion, continues working and lower manufacturing cost. Also robots improve labors conditions in automotive production lines so that they can do repetitive processes and pieces displacement by minimum possible force. Regarding to reduction of labors, multi purpose...
This article presents a new approach for Structural Learning of Neurofuzzy (NF-) GMDH networks, based on Genetic Algorithm (GA) optimization. Conventional methods, prune unnecessary links and units from the large network by minimizing the derivatives of the partial description. In proposed method pruning of needless links, units and fuzzy rules in each partial description, has been done by adding...
this paper present power system load frequency control by modified dynamic neural networks controller. The controller has dynamic neurons in hidden layer and conventional neurons in other layers. For considering the sensitivity of power system model, the neural network emulator used to identify the model simultaneously with control process. To have validation of proposed structure of neural network...
This paper introduces a new approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO) with some modification in it to the training of all parameters of ANFIS structure. These modifications...
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with recursive least square (RLS) for training. We use PSO with...
In this paper, we present n new automated system for on-line monitoring and detection of ST changes in one channel electrocardiograms (ECG). This system consists of a preprocessing step for QRS detection, baseline wandering removal, and noise suppression. In the next step, the system uses a normal beat template as reference and a set of rules defined by cardiologists for detecting ischemic beats based...
Tracking control of mobile robots has many research interests among academic researchers [1],[6],[7]. This subject has opened many different aspects of research studies for different purposes such as obstacle avoidance, trajectory tracking, vision based tracking, etc. The results are being used in different autonomous vehicles from autopilot systems to little discovery mobile robots. In this paper...
In this paper a fuzzy neural network (FNN) is presented for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearties and internal friction. The system contains several major nonlinearties that limit the ability of simple controllers in achieving satisfactory performance. These nonlinearties include: valve dead zones, valve flow saturation, and cylinder seal friction...
Fuzzy c-mean (FCM) is a common clustering algorithm which is used for segmentation of magnetic resonance (MR) images. However in the case of noisy MR images, efficiency of this algorithm considerably reduces. Recently, researchers have been introduced two new parameters in order to improve performance of traditional FCM in the case of noisy images. New parameters are computed using artificial neural...
In this article using a population-based method, particle swarm optimization in training the standard deviation and centers of radial basis function fuzzy neural networks is put into practice and the results are compared with training the same networks' standard deviation and centers using backpropagation. We have applied Least Square and Recursive Least Square in training the weights of this fuzzy...
Locally linear model tree algorithm is one of the useful techniques in modeling of complex nonlinear systems. One of the important features in the incremental algorithms such as LOLIMOT is the structure optimization of the model. In this paper, the merging algorithm is used as a supervisor of the original LOLIMOT to overcome suboptimal LLMs. Also, particle swarm optimization is used to obtain the...
In this paper a novel hybrid strategy is employed in order to improve the controller performance. The main idea is combination of classical and intelligent controllers. Feedback error learning (FEL) as a two degrees of freedom (2DOF) control scheme, has been introduced based on this idea. This paper takes a step ahead of traditional FEL schemes which combine a PID controller with an intelligent inverse...
This paper addresses new classic approaches for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we used the classical method based-on Lyapanov function. It is demonstrated that this new technique have good control ability performance. It is shown that this technique can be successfully used to stabilize any...
This paper has introduced a new method for feature subset selection to which less attention has been given. Most of the past works have emphasized feature extraction and classification using classical methods for these works. The main goal in feature extraction is presented data in lower dimension. One of the popular methods in feature extraction is principle component analysis (PCA). This method...
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some...
This paper introduces a new structure in neural networks called TD-CMAC, an extension to the conventional Cerebellar Model Arithmetic Computer (CMAC), having reasonable ability in time series prediction. TD-CMAC, the conventional CMAC and a classical neural network model called Multi-Layer Perceptron (MLP) are simulated and evaluated for 1-hour-ahead prediction and 24-hour-ahead prediction of carbon...
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