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The paper presents selected properties of the adaptive speed neural controller trained online for direct drive during mechanical changes of the object parameters. In the article was compared different algorithms for learning neural networks such as: backpropagation algorithm BP, momentum backpropagation MBP, Quickprop and RPROP. The authors proposed an effective method of supervision of learning neural...
Decision making tasks that involve processing of sequential stimuli with long delays pose a significant challenge to modeling using current methods in neural networks. However, decision making in animals involves storage of salient stimuli over long periods of time, robust maintenance of this information in the presence of noisy input, and subsequent recall and processing at the time of final decision...
Artificial Neural Network (ANN) forms a useful tool in pattern recognition tasks. Collection of five, eight or more cards in a cards game are normally called poker hands. There are various poker variations, each with different poker hands ranking. In the present paper, an attempt is made to solve poker hand classification problem using different learning paradigms and architectures of artificial neural...
This paper presents the use of artificial neural networks (ANN) to determine the solution one of the classic applications of differential equations, the mixing tank problem. An artificial neural network with feed-forward backpropagation is designed to predict the concentration of substance in the tank at any time t. The network has three layers of structure 5 - 10 - 2 and used the Levenberg-Marquadt...
The usage of IEEE 802.15.4, Wireless Sensor Networks in our daily life has shown exponential growth in the past decade. Localization of wireless sensor networks is the most critical aspects of this network. One of the major models used in localization, uses Multilayered Perceptron for training its data. This paper focuses on the impact of various MLP training functions on range based localization...
Feed forward Multilayer Perceptron (MLP) Neural Networks are universal approximators. Weight adjustment of the connectionist model is crucial to architectures that model systems behavior. This paper developed a neural network for hydrological purposes. Two architectures were developed, investigated, and tested for forecasting rainfall in the rain-fed Sectors in Sudan. A monthly architecture and a...
User localization information is an important data source for ubiquitous assistance in smart environments. This paper proposes a device-free passive user localization approach based on room-equipped passive RFID instead of battery powered hardware. Based on this approach recent work tried to formulate physical model based localization algorithms. These approaches suffer from their inability of integrating...
The increasing growth in the use of DC Servomotors for multiple industrial applications in the last few decades have made them one of the most important system's drives. Therefore, developing an intelligent DC Servomotor position control scheme and, in particular, a DC Servomotor Neural Model based on a well-defined mathematical model that can be used for off-line simulation are very important tools...
The operation principles of proton exchange membrane (PEM) fuel cell system relate to thermodynamics, electrochemistry, hydrodynamics, mass transfer theory, which form a complex nonlinear system, and it is different to establish its mathematical model. This paper utilizes the approach and self-study ability of artificial neural network to build a model of nonlinear system, and adapts the modified...
The ever increasing need for energy efficient systems has led to various ingenious ideas about energy management. A major offshoot of this search for energy efficient solutions is demand management in power systems. The goal of any demand management program is to control the demand for electric power among customers thereby creating load relief for electric utilities and improving system security...
BP neural network (back propagation neural network) is a mathematical model for machine learning. It has a strong advantage in terms of prediction of the future events, and taking into account the different applications, its impact factors are different, which makes the model complex and diverse. A general modeling approach is proposed, which creates and stores BP neural network model dynamically,...
The basic principle of Artificial Neural Networks and BP algorithm was introduced in this paper. The application of BP algorithm Artificial Neural Networks in fault diagnosis of 40TM liquid-gas hammer was studied. The superiority of BP algorithm Artificial Neural Networks in fault diagnosis was proved by the MATLAB simulation and the training. The causes of faults were determined by BP algorithm Artificial...
This paper introduces a method for the fault diagnosis of a rotor system. For a vibration signal of a rotor system fault, an AR model is established first, and then the related parameter and amplitude spectrum of this mode can be obtained, etc. The experiments show the above-mentioned method can effectively diagnose the fault of a rotor system.
Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage...
Aiming at the difficulty of tank unit combat formation recognition in virtual simulation training, the recognition method based on BP neural network is put forward. After analyzing the definition and character of the tank unit combat formation, the recognition strategy for tank unit formation is put forward. Then the recognition model based on BP neural network is built. In order to get plentiful...
Established the computational model about the safe distance of vehicles. In order to simulate the dynamic model of rear-end, based on VB software to build a freeway rear-end simulation system. Simulation system provides an important means for in-depth study on rear-end probability. To investigate the non-linear relationship of probability and impact factors of rear-end, established probability of...
This paper takes a kind of ceramic glaze as an example, and builds an improved BP neural network model for optimizing the formulation on ceramic glaze. The improved BP neural network adopts Levenberg-Marquardt algorithms. The paper reviews how to build the ceramic formulation optimization model based on BP artificial neural network, including the establishment of neural network, the training, and...
Ultrasonic techniques are wildly used in online partial discharge (PD) location and recognition for electrical transformers. This paper focuses on a new ultrasonic feature extraction method. The normalized discharge grey moment features are extracted from ultrasonic signals to perform PD recognition. These features are sent to an improved BP neural network as to perform pattern recognition. Two types...
In this paper, a novel two hidden layers artificial neural network (2HLANN) model is proposed to predict the dynamic nonlinear behavior of wideband RF power amplifiers (PAs). Starting with a generic low-pass equivalent circuit of the PA, several circuit transformations are applied in order to build an appropriate artificial neural network structure and improve the modeling accuracy. This approach...
Established the computational model about the safe distance of vehicles. In order to simulate the dynamic model of rear-end, based on VB software to build a freeway rear-end simulation system. Simulation system provides an important means for in-depth study on rear-end probability. To investigate the non-linear relationship of probability and impact factors of rear-end, established probability of...
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