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This paper presents one strategy for modeling and optimization of a microdilling process. Experimental work has been carried out for measuring the thrust force for five different commonly used alloys, under several cutting conditions. An artificial neural network-based model was implemented for modelling the thrust force. Neural model showed a high goodness of fit and appropriate generalization capability...
This paper proposed a concept of the multi-agent neural network, where neurons are agents of the multi-agent system. Each agent has a decision-making system that rests upon the system of production rules. A multi-chromosome genetic algorithm was developed. This algorithm allows to train a multi-agent neural network simultaneously by several parameter classes of the target function. Efficiency of the...
Search engines resolve the most informational needs of users by indexing huge amounts of data from web pages. In this process, spam pages prevent users from reaching their desirable results. Spam pages use deceptive methods to get a higher rank than their real one in search engines. For a human expert, recognition of spam pages is an easy task, but it is too complicated for a machine. Regarding the...
The paper deals with a soft computing state control method for multi input — multi output (MIMO) non-linear dynamic model of a robot. Soft methods based on neural networks and genetic algorithms have proven their effectiveness for this application. They are based on quite simple principles, but take advantage of their mathematical nature: non-linear iterative computation solutions. One way of controlling...
In this paper, a distributed photovoltaic (PV) power forecasting method is proposed by using genetic algorithm based neural network approach. With the large-scale application of PV power generation in the applications of society, and the characteristic of volatility and intermittent, and power forecasting of PV distributed have played a more important role in research of control strategies for microgrid...
In this paper, we have proposed a fuzzy interval time series model using a new strategy to replace the conventional defuzzification step, where genetic algorithm has been used to optimize the interval parameters and neural network has been used to learn the trend of the time series. First order fuzzy time series with equal time interval has been used on two data sets, enrollments of the University...
A new aerodynamic parameter fitting approach is proposed to avoid online aerodynamic parameter interpolation for advanced flight vehicle trajectory generation, guidance and control. Due to its ability to fit any nonlinear function and simple structure, BP neural network was chosen as the tool to fit the aerodynamic parameters which are the function of Mach number, angle of attack and other variables...
This paper studies the fault diagnosis of inertia navigation unit which plays an important role in inertia navigation system. The method chosen in the fault diagnosis is combined Genetic Algorithm and wavelet neural network. Wavelet transform will effectively handle the collected inertia navigation unit signal. The characteristic signals extracted will be regarded as inputs to the neural network....
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 proposes a distributed Neural/Genetic algorithm able to compute both the more suitable positioning and transmission modulation schemes for fixed/mobile wireless nodes equipped with software defined radio abilities. Devices considered in this work are able to move towards new positions by applying the concept of controlled mobility. The selection of the more suitable modulation scheme is...
In this paper we train an Artificial Neural Network (ANN) using Memetic Algorithm (MA) and evaluate its performance on the UCI spambase dataset. The Memetic algorithm incorporates the local search capacity of Simulated Annealing (SA) and the global search capability of Genetic Algorithm (GA) to optimize the parameters of the ANN. The performance of the MA is compared with traditional GA in training...
Brain hemorrhage detection and classification is a major help to physicians to rescue patients in an early stage. In this paper, we have tried to introduce an automatic detection and classification method to improve and accelerate the process of physicians' decision-making. To achieve this purpose, at first we have used a simple and effective segmentation method to detect and separate the hemorrhage...
The study of vessel collision risk index and decision-making system of vessel collision avoidance has always been at the center of concern for all sailors and navigators. The article proposes the GA-BP algorithm. That is a combination of the Genetic Algorithm and the Neural Network. Then the article use it in the computing of the vessel's Collision Risk Index by multiple parameters. It is indicated...
This study develops an evolutionary strategy called DEPSO-GANN, which uses an artificial neural network (ANN) based on a parallel genetic algorithm (PGA) with migration for the adaptive control of integrated differential evolution (DE) and particle swarm optimization (PSO) to solve large-scale optimization problems, reduce calculation costs, and improve the stability of convergence towards the optimal...
At present, as a method of establishing mathematical model of the system, the system identification has been widely applied to the automatic control, aviation, space flight, astronomy, medicine, biology, marine ecology and society, economics and many other fields. With the rapid development of science and technology, the status of system identification technique in various disciplines is becoming...
One of the challenges research on model based fault detection and diagnosis of a system is finding the accurate models. In this paper, fuzzy logic based model using genetic algorithm for optimizing the membership function is used in the development of fault detection and diagnosis of a process control rig. The model is used to generate various residual signals, which relate to the faults of the system...
Based on the nonlinearity of typhoon intensity, the Locally Linear Embedding (LLE) method is employed to reduce the dimensions of the factors obtained from the climatology and persistence (CLIPER) prediction method for predicting typhoon intensity in the Western Pacific Ocean (WPO). Emulating the theory of the numerical weather prediction in ensemble forecast, a new nonlinear artificial intelligence...
Host Intrusion detection systems (HIDS) are increasingly emerging techniques for information security on host based applications. These systems should be designed to prevent unauthorized access of system resources and data. Many intelligent learning techniques are currently being applied to the large volumes of data for the construction of an efficient host intrusion detection system. This paper represents...
In order to meet the requirement of high efficiency and low emission in boiler operations, a hybrid model is established based on experimental data and combined with BP neural network. This model uses the adjustable operation parameters of boiler as inputs and chooses NOx emission and boiler efficiency as outputs to achieve the prediction of NOx emission and thermal efficiency. And it optimizes the...
A new approach for modulation of an 11-level cascade multilevel inverter using selective harmonic elimination is presented in this paper. The dc sources feeding the multilevel inverter are considered to be varying in time, and the switching angles are adapted to the dc source variation. This method uses genetic algorithms to obtain switching angles offline for different dc source values. Then, artificial...
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