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Models of adaptive behaviour are normally highly complex due to a greater emphasis on, learning by interaction with the environment, flexible behaviour, and, an evolutionary learning methodology. This research investigates how a combination of Genetic Algorithms and Neural Networks (GA+NN) can be used to model behaviour to solve a task requiring co-operation between two artificial autonomous agents...
Fuzzy controllers are knowledge based and for many real world processes it is possible to design a fuzzy controller which provides bounded regulation using only a heuristic approach. However, in order to achieve satisfactory performance it is always necessary to carry out complicated procedures of fine tuning. In this paper, a fuzzy controller is implemented in a neural structure which then provides...
Cognitive radio enabled dynamic spectrum access is a promising solution to alleviate the spectrum low-utilization problem. Secondary users need to sense the bands before transmitting on them to avoid collision with the licensed users. To reduce delay and energy consumption of spectrum sensing, spectrum prediction is incorporated to predict the future usage of channels before spectrum sensing. In this...
This paper presents a control scheme, for automatic generation control (AGC) of both two and three unequal area thermal system, having reheat turbines and generation rate constraints, based on multilayer perceptron neural network (MLPNN) whose weights are updated using reinforcement learning. The weights of MLPNN controller are adjusted dynamically onlinely using backpropagation updation technique...
Wireless Ad Hoc Networks are capable of communication through wireless medium without the need for a pre-existing infrastructure. Much effort has gone into mobile ad-hoc network (MANET) research over the past decade. Yet, even today, mobile ad-hoc networking is seen as a relatively new area of research. The reason for this can be traced to the fact that the maturity in truly understanding these networks...
This paper analyzed the enterprise process energy consumption systematically with a lot of statistic data starting from energy efficiency, and established the energy consumption prediction model based on genetic algorithm of wavelet neural network (GA-WNN). This paper made previous optimization training with genetic algorithm, which have feature of natural evolution regularity, to the weights and...
Back Propagation (BP) neural network was optimized by Genetic Algorithm (GA) to be Genetic Algorithm Optimized Back Propagation (GA-BP) neural network. The data of Monte Carlo simulations was used to train BP neural network and GA-BP neural network. The accuracy of each neural network was investigated and compared. The result showed GA-BP neural network was much steady and more accurate. A laser source...
In this paper, from the Angle to predict, take hydro-generating operation condition parameters (head, power) as input sample, take unit head cover vibration as output sample, create BP and GANN neural network prediction model. Train the established models, through comparing the two models. GANN model Has better precision.
This paper established a back propagation (BP) neural network tandem cold rolling force prediction model, and optimized by genetic particle swarm algorithm (GPSA). Genetic particle swarm algorithm has the advantage of both genetic algorithm (GA) and particle swarm algorithm (PSO) algorithm, integrates global searching ability with high convergence speed. Taking neural network weights and threshold...
Through the application of genetic algorithms (genetic algorithm, simplified as GA) and BP(Back Propation) neural network, I built a prediction model of roses diseases, in which I choose six indicators as the input of network, they are the minimum temperature, maximum temperature, average temperature, minimum humidity, maximum humidity, average humidity in the greenhouse, then I choose three diseases...
In order to obtain the law of the building settlement and forecast it effectively, neural network model was established for building settlement forecasting based on measured data, and an engineering example is shown to test and verify. Firstly, data of building settlement measured were normalized; embedding dimension was selected to establish the leaning samples. Mean square error (MSE) and mean absolute...
With regard to the design of ultimate vertical bearing capacity of single rock-socketed pile, theoretical formula recommended by "Technical code for building foundations" (JGJ 94-2008)and static loading test are the two most popular methods. But results obtained from this two approaches have great differences. The average of relative difference (note: taking absolute value) is even up to...
In this paper, we propose an effective four-stage approach that detects fire automatically. The proposed algorithm is composed of four stages. In the first stage, an approximate median method is used to detect moving regions. In the second stage, a fuzzy c-means (FCM) algorithm based on the color of fire is used to select candidate fire regions from these moving regions. In the third stage, a discrete...
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,...
In this paper, an efficient scheme to detect the unprecedented changes in system reliability and find the failed component state by classifying the faults is proposed using kalman filter and hybrid neuro-fuzzy computing techniques. A fault is detected whenever the moving average of the Kalman filter residual exceeds a threshold value. The fault classification has been made effective by implementing...
The conventional routes of optimizing boiler operational parameters were mainly according to boiler design value and historically optimum value, but these methods had certain limitation in the real-time renewal and excavating optimization potential. This paper takes the circulating fluidized bed boiler (CFB) as the research object, by using the Absolute Mean Impact Value (AMIV) to optimize the structure...
Through analyzing the operating mechanism of the genetic algorithm, intelligent adaptive genetic algorithm (IAGA) is proposed whose crossover probability and mutation probability can be adjusted adaptively. Then IAGA is applied to optimize the weights and thresholds of the forward neural network, and establish soft-sensor model of gasoline endpoint of the main fractionator of fluid catalytic cracking...
This paper is mainly concerned with the development of a control method for a permanent-magnet synchronous motor (PMSM) which runs in the conditions of a networked control system (NCS). A middleware is implemented and modifies the output of the controller based on gain scheduling. The gain scheduling is designed by using an artificial neural network, which is first trained offline to learn the relation...
This paper seeks to implement and test a financial forecasting agent which employs time series, derived time series data, and news that are retrieved and extracted from the Web. This research focuses on the time series data of some individual stocks from the Indonesian Stock Exchange as well as the index data. The financial forecasting agent implemented is based on a Multilayer Neural Network trained...
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