Objective: This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller. Meth...
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Objective: This manuscript describes the use of a hardware-in-the-loop simulation to simulate the control of a multivariable anesthesia system based on an interval type-2 fuzzy neural network (IT2FNN) controller. Methods and materials: The IT2FNN controller consists of an interval type-2 fuzzy linguistic process as the antecedent part and an interval neural network as the consequent part. It has been proposed that the IT2FNN controller can be used for the control of a multivariable anesthesia system to minimize the effects of surgical stimulation and to overcome the uncertainty problem introduced by the large inter-individual variability of the patient parameters. The parameters of the IT2FNN controller were trained online using a back-propagation algorithm. Results: Three experimental cases are presented. All of the experimental results show good performance for the proposed controller over a wide range of patient parameters. Additionally, the results show better performance than the type-1 fuzzy neural network (T1FNN) controller under the effect of surgical stimulation. The response of the proposed controller has a smaller settling time and a smaller overshoot compared with the T1FNN controller and the adaptive interval type-2 fuzzy logic controller (AIT2FLC). The values of the performance indices for the proposed controller are lower than those obtained for the T1FNN controller and the AIT2FLC. Conclusion: The IT2FNN controller is superior to the T1FNN controller for the handling of uncertain information due to the structure of type-2 fuzzy logic systems (FLSs), which are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the FLSs. (C) 2014 Elsevier B.V. All rights reserved.
Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worse...
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ISBN:
(纸本)9781479979738
Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using K-Means Clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.
Biometric technology plays a vital role for providing the security which is imperative part in secure system. Human face recognition is a potential method of biometric authentication. This paper presents a process of ...
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ISBN:
(纸本)9781479948192
Biometric technology plays a vital role for providing the security which is imperative part in secure system. Human face recognition is a potential method of biometric authentication. This paper presents a process of face recognition system using principle component analysis with backpropagation neural network where features of face image has been combined by applying face detection and edge detection technique. In this system, the performance has been analyzed based on the proposed feature fusion technique. At first, the fussed feature has been extracted and the dimension of the feature vector has been reduced using Principal Component Analysis method. The reduced vector has been classified by backpropagation neural network based classifier. In recognition stage, several steps are required. Finally, we analyzed the performance of the system for different size of the train database. The performance analysis shows that the efficiency has been enhanced when the feature extraction operation performed successfully. The performance of the system has been reached more than 92% for the adverse conditions.
In this paper, a new method of filtering is introduced which can be adopted to neural networks, which can be applied to improve the corrupted images by salt-pepper noises. In the first step, neural networks are used t...
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ISBN:
(纸本)9781889335490
In this paper, a new method of filtering is introduced which can be adopted to neural networks, which can be applied to improve the corrupted images by salt-pepper noises. In the first step, neural networks are used to identify the location of the noises in image and in the next step;the identified noisy pixel will be reduced by using Gaussian recursive filter. This method is called the Neural Network Gaussian (NNG) filter. Using neural networks to recognize the location of salt-pepper noises prevents incorrect recognition of noise and increase the quality of noise reduction process. Moreover, by using Gaussian recursive filters against the typical median filter, which used only to omit trivial noises, the algorithm performance will also be improved significantly.
A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature ...
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A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.
This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN s...
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This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN system can construct and learn its knowledge base from the input-output training data firstly. Thus, a nonlinear system can be represented by several if-then rules with Gaussian membership functions and TSK-type consequent parts. Based on the learned TFNN system, a robust fuzzy controller is proposed, which combines linear matrix inequality-based fuzzy controller and fuzzy sliding model controller. Rigorous proof of asymptotic stability for the closed-loop system is presented via Lyapunov stability theorem. Several examples are presented to illustrate the effectiveness of our approach.
This paper aims at verifying the accuracy of Artificial Neural Networks (ANN) in assessing the transient stability of a single machine infinite bus system. The fault critical clearing time obtained through ANN is comp...
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ISBN:
(纸本)9781467362313;9781467362320
This paper aims at verifying the accuracy of Artificial Neural Networks (ANN) in assessing the transient stability of a single machine infinite bus system. The fault critical clearing time obtained through ANN is compared to the results of the conventional equal area criterion method. The multilayer feed-forward artificial neural network concept is applied to the system. The training of the ANN is achieved through the supervised learning;and the backpropagation technique is used as a learning method in order to minimize the training error. The training data set is generated using two steps process. First, the equal area criterion is used to determine the critical angle. After that the swing equation is solved using the point-to-point method up to the critical angle to determine the critical clearing time. Then the stability of the system is verified. As a result we find that the critical clearing time is predicted with slightly less accuracy using ANN compared to the conventional methods for the same input data sets unless the ANN is well trained.
In order to improve the accuracy and reliability of prediction of deformation monitoring data, a hybrid modeling and forecasting approach based on autoregressive model (AR) and the back-propagation (BP) neural network...
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ISBN:
(纸本)9783037855652
In order to improve the accuracy and reliability of prediction of deformation monitoring data, a hybrid modeling and forecasting approach based on autoregressive model (AR) and the back-propagation (BP) neural network is proposed to forecast the deformation. The results of experiments show that this method can forecast the deformation precisely, and it is more suitable for those occasions where the deformation monitoring data should meet the high demand.
Neural networks, or the artificial neural networks to be more precise, represents a technology that is rooted in many disciplines: neuroscience, mathematics, statistics, physics, computer science and engineering. Neur...
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ISBN:
(纸本)9783642315510
Neural networks, or the artificial neural networks to be more precise, represents a technology that is rooted in many disciplines: neuroscience, mathematics, statistics, physics, computer science and engineering. Neural network finds applications in such fields as modeling, time series analysis, pattern recognition signal processing and control by virtue of an important property: the ability to learn from input data with or without a teacher An a biological system, learning involves adjustments to the synaptic connections between neurons same for artificial neural networks (ANNs) works too that has made it applicable to valid applications. Neural Network architecture has the ability to learn for the things and then later on classify the things. Neural Network for Character Recognition is based over Multi layered Architecture having back-propagation algorithm. First Network is been trained for the alphanumeric handwritten characters and then testing the network with the trained or untrained handwritten characters. We achieved a greater computation enhancement by using modified back- propagationalgorithm having an added momentum term, which lowers the training time and speeds the system. The time is more reduced with its parallel implementation using CUDA.
This paper compares the performance of Gradient descent with momentum & adaptive backpropagation (TRAINGDX) and BFGS quasi-Newton backpropagation (TRAINBFG) of backpropagationalgorithm in multilayer feed forward ...
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ISBN:
(纸本)9781467359863;9781467359870
This paper compares the performance of Gradient descent with momentum & adaptive backpropagation (TRAINGDX) and BFGS quasi-Newton backpropagation (TRAINBFG) of backpropagationalgorithm in multilayer feed forward Neural Network for Handwritten English Characters of Vowels. This analysis is done with five samples of Handwritten English Characters of Vowels collected from five different people and stored as an image. After partition these scanned image into 4 portions, the densities of these images are determined by using MATLAB function. An input pattern will use these 4 densities of each character as an input for the two different Neural Network architectures. In our proposed work the Multilayer feed forward neural networks will train with two learning algorithms;those are the variant of backpropagation learning algorithm namely Quasi-Newton backpropagation learning algorithm and Gradient descent with momentum and adaptive backpropagation learning algorithm for training set of the Handwritten English Characters of Vowels. The performance analysis of both Neural Network architectures is done for convergence and nonconvergence. Different observations have been considered for trends of error in the case of nonconvergence. From the observation of the result, it can be shown that in the performance of these above two learning algorithms with the training set of handwritten characters of Vowels, there is limitation of gradient descent learning algorithm for convergence due to the problem of local minima which is inherit problem of backpropagation learning algorithm.
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