For online control of various dynamical systems, an adaptive artificial neural network (ANN) based proportional integral derivative (PID) controller is developed. For linear time invariant processes, conventional PID ...
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ISBN:
(纸本)9781467385886
For online control of various dynamical systems, an adaptive artificial neural network (ANN) based proportional integral derivative (PID) controller is developed. For linear time invariant processes, conventional PID controller is suitable but they have limitations when they are required to control the plants having high non linearity or their parameters are changing with the time. In order to find the parameters of PID controller, information regarding the dynamics of the plant is essential. If perturbation occurs in plant parameter(s) then PID controller may work only if these changes are not severe. But most plants are either non linear or their parameters changes with time and this demands for a use of more robust type of controller and ANN is a suitable candidate. To use the power of PID controller and ANN, ANN based PID controller is proposed in this paper. The benefit of this combination is that it utilizes the simplicity of PID controller mathematical formula and uses the ANN powerful capability to handle parameter variations and non linearity.
作者：
A.S. AneeshkumarC. Jothi VenkateswaranResearch Scholar
PG and Research Department of Computer Science Presidency College Chennai 600 005 India Dean
Department of Computer Science & Applications Presidency College Chennai 600 005 India
The application of medical data mining is emphasis with temporal data in this piece of writing. The application of computational methods in medical field is well known and which started in previous decades, but still ...
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The application of medical data mining is emphasis with temporal data in this piece of writing. The application of computational methods in medical field is well known and which started in previous decades, but still now also lot of researches take place in artificial intelligence, knowledge discovery and mining. Correlating computational intelligence with medical intelligence to predict negatively influenced epidemiological factors in liver disorder patients.
Iris recognition is the highly trusted identification recognition technology among methods of biological recognition. In this paper, we use the back propagation algorithm to train the neural network, so as to establis...
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Iris recognition is the highly trusted identification recognition technology among methods of biological recognition. In this paper, we use the back propagation algorithm to train the neural network, so as to establish the iris recognition system model. The experiment demonstrates that it has a high recognition rate and the recognition speed is reasonable. The proposed method provides a convenient way for iris recognition.
This paper presents a new approach for short term electrical load forecasting (STLF) using artificial neural networks (ANN), and examines the feasibility of various mathematical models for STLF. To make these mathemat...
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ISBN:
(纸本)9781479959044
This paper presents a new approach for short term electrical load forecasting (STLF) using artificial neural networks (ANN), and examines the feasibility of various mathematical models for STLF. To make these mathematical models to yield satisfactory and acceptable results, various system models are formulated considering various combination of parameters like base load component, day of the week, load inertia, short term trends, auto-correlation, length of the past data, etc. Various modifications of back propagation algorithm (BPA) have been proposed, to explore the ideal combination that suit the forecasting need of large utilities like regional electricity grids. Further, the load dynamics are extensively studied to identify the parameters for system modeling.
Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alt...
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Because of the current depletion of high grade reserves, beneficiation of low grade ore, tailings produced and tailings stored in tailing ponds is needed to fulfill the market demand. Selective flocculation is one alternative process that could be used for the beneficiation of ultra-fine material. This process has not been extensively used commercially because of its complex dependency on process parameters. In this paper, a selective flocculation process, using synthetic mixtures of hematite and kaolinite in different ratios, was attempted, and the ad-sorption mechanism was investigated by Fourier transform infrared （FTIR） spectroscopy. A three-layer artificial neural network （ANN） model （4？4？3） was used to predict the separation performance of the process in terms of grade, Fe recovery, and separation efficiency. The model values were in good agreement with experimental values.
Shot peening is a process of cold working a part that increase its resistance to metal fatigue and some forms of stress *** peening causes plastic deformation in the surface of the peened part and leads some changes i...
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Shot peening is a process of cold working a part that increase its resistance to metal fatigue and some forms of stress *** peening causes plastic deformation in the surface of the peened part and leads some changes in mechanical and metallurgical properties of *** intelligence(AI) systems such as artificial neural networks(ANNs) have found many applications to predict and optimize the engineering problems in the last few *** present study effects of SP on mechanical and metallurgical properties of 18 CrN i Mo7-6 are investigated by *** has been developed based on backpropagation error *** order to train the network data of experimental tests results were *** tests were concluding different SP types: single step SP and dual step SP with different SP *** of the ANN is accomplished using experimental data not used during networks *** from the surface and Almen intensity are considered as input parameters and residual stress,remnant austenite content,Cauchy breath,domain size and microhardness are regarded as output parameters of the *** comparison of obtained results of ANN's response and experimental values indicates that the networks are tuned well and the ANN can be used to predict the SP effects on mechanical and metallurgical properties of materials.
Use of power electronic converters with nonlinear loads leads to power quality problems by producing harmonic currents and drawing reactive power. A shunt active power filter provides an elegant solution for reactive ...
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ISBN:
(纸本)9781509000777
Use of power electronic converters with nonlinear loads leads to power quality problems by producing harmonic currents and drawing reactive power. A shunt active power filter provides an elegant solution for reactive power compensation as well as harmonic mitigation leading to improvement in power quality. However, the shunt active power filter with PI type of controller is suitable only for a given load. If the load is varied, the proportional and integral gains are required to be fine tuned for each load setting. The present study deals with hybrid artificial intelligence controller, i.e. neuro fuzzy controller for shunt active power filter. The performance of neuro fuzzy controller over PI controller is examined and tabulated. The salvation of the problem is extensively verified with various loads and plotted the worst case out of them for the sustainability of the neuro fuzzy controller.
This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the back propag...
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This paper presents a particle swarm optimization (PSO) technique to train an artificial neural network (ANN) for prediction of flank wear in drilling, and compares the network performance with that of the backpropagation neural network (BPNN). This analysis is carried out following a series of experiments employing high speed steel (HSS) drills for drilling on mild steel workpieces, under different sets of cutting conditions and noting the root mean square (RMS) value of spindle motor current as well as the average flank wear in each case. The results show that the PSO trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.
Recently wide applications of neural networks are reported in geophysical scientific papers, mostly lack the consideration of their mathematical evaluation and performance. In these general estimators/regression funct...
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Recently wide applications of neural networks are reported in geophysical scientific papers, mostly lack the consideration of their mathematical evaluation and performance. In these general estimators/regression function/classifiers, parameters to be tuned are the number of layers, neurons, type of transfer function, minimum size of training set, etc. These will be carefully tuned per each physical problem. Among all, the number of hidden layers and the number of neurons in each hidden layer are the two important parameters to be decided and normally no rules are available for finding them precisely. In this paper a method to find the hidden layer size is described beside the main purpose of the paper which is to compare the performance of the first break picker networks. We used a known learning-curve and introduce a measure named "neuron-curve" to find the optimal layer size & minimum size of training set. This paper shows the application of these two curves in finding the first break picks of seismic refraction data. Furthermore, the effect of noise on the architecture of two known neural networks (multilayer perceptron and radial basis function) in the first break picking is also investigated. (C) 2014 Elsevier B.V. All rights reserved.
Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evol...
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Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if-then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models. (C) 2013 Elsevier Ltd. All rights reserved.
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