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This paper presents a novel approach for the generation of 3D building model from IKONOS satellite image data. The main idea of 3D modeling is based on the grouping of 3D line segments. The divergence-based centroid neural network is employed in the grouping process. Prior to the grouping process, 3D line segments are extracted with the aid of the elevation information obtained by using area-based...
This paper presents the retro-propagation algorithm for tuning the parameter of Artificial Neural Networks used by pharmachemical industry. The numerical test results obtained on lubrication and air circuits shown that the proposal improve the performance in terms of number of iterations and reliability of the models. BEKER Laboratories production line, is a Pharmaceutical production company located...
This paper proposed an artificial neural network (ANN) approach based on Lagrangian multiplier method (Lagrangian ANN) to solve the problem of economic load flow in a power system. Operational requirements and transmission losses are also taken care by the proposed approach. Power plant operating costs are represented by exponential cost functions. Simulation on a test example with six generating...
This paper uses generalized congruence function instead of transfer function of classical BP neural network, and improve convergence rate of neural network. We introduce the subsection generalized derivation, error back propagation derivation mechanism of classical BP algorithm to adjust weight vector in generalized congruence neural network, and modify generalized congruence neural network, and then...
According to the floatation processing characteristic with time-variation, uncertainty and complicated nonlinear relations, a prediction method of concentrate grade and prediction model of ore dressing date is proposed. This article establish a prediction model of ore dressing date based on Jordan neural network including input of influence factors and dynamic time sequence feedback of concentrate...
After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network...
To build a precise and real time model is a key issue in fulfilling on optimal control of marine power station diesel engine in the hardware-in-the-loop simulation system. In this paper, a control oriented real time model for marine power station diesel engine is proposed based on neural network. After establishing the quasi-steady model of diesel, the hybrid model based on the compensation of neural...
Local minimum is incorporated problem in neural network (NN) training. To alleviate this problem, a modification of standard backpropagation (BP) algorithm, called BPCL for training NN is proposed. When local minimum arrives in the training, the weights of NN become idle. If the chaotic variation of learning rate (LR) is included during training, the weight update may be accelerated in the local minimum...
In real-time, it is highly essential to have an autonomous translator that can process the images and recognize the signs very fast at the speed of streaming images. In this paper, architecture is being proposed using the neural networks identification and tracking to translate the sign language to a voice/text format. Introduction of Point of Interest (POI) and track point provides novelty and reduces...
Texture classification is an important and challenging factor in image processing system which refers to the process of partitioning a digital image into multiple constituent segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Artificial Neural Network (ANN) Based texture classification or Segmentation...
Software development effort estimation is one of the most major activities in software project management. A number of models have been proposed to make software effort estimations but still no single model can predict the effort accurately. The need for accurate effort estimation in software industry is still a challenge. Artificial Neural Network models can be used for carrying out the effort estimations...
Adaptive Neuro-Fuzzy Inference System (ANFIS) has become a popular tool in neuro-fuzzy modeling. However, since it includes many parameters needed to be set, its designing process is a complicated and time-intensive task for experimenters. To tackle this problem, in this paper we implement the Design of Experiment (DOE) technique to identify the significant parameters of ANFIS when it applies to the...
An important issue in design and implementation a neural network is that perturbations of training pattern pairs may cause some disadvantages to outputs. How the perturbations of training pattern pairs in Morphological Bidirectional Associative Memories (MBAMs) influence on the outputs is discussed in this paper. We define the outputs' max error to evaluate the robustness of the MBAMs. The related...
Soil water is the basic condition of crop living. Soil water evaporation is not only main foundation of the management for water but also have an important effect on the regulation for temperature, humidity, environment and energy consumption in the greenhouse. Soil water even affect crop yields, qualities, and economy benefit of crop production. This paper analyzed main environment factors caused...
In different conditions such as light and complex backgrounds, we get some car images, the traditional methods are slow convergence speed and low accuracy. This paper presents a method which applies fuzzy theory to enhance several features of for target. To obtain the license information, we use an improved BP neural network algorithm, by through setting proper numbers of hidden layer of BP network,...
The paper first makes a thorough research on the method for analog circuit fault diagnosis based on kurtosis and negentropy, and then theoretically analyses it's advantage and disadvantage, which is followed by introducing the idea of centroid to overcome the method's shortcoming, making the improved method can extract the signal's feature more efficiency. Finally, it applies the improved method to...
Genetic algorithm (GA) is applied to select main affecting factors of coal and gas outburst to solve the over-fitting problem of BP neural network (NN) in predicting coal and gas outburst, and a modified BP NN predictor is established, which input variables are the factors selected. In our GA, chromosome is a binary encoding which each gene corresponds to a variable, penalty function is introduced...
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
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...
Vibration faults frequently occurring to the feed water pump in large-scale power plants are diagnosed by the integrated neural network based on MATLAB. The integrated neural network for fault diagnosis is established from individual neural network and on the basis of information fusion. The strategies and principles for the realization and formation of integrated neural network are analyzed and a...
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