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The flight control system (FCS) is a complex nonlinear multi-input and multi-output) system, it is very difficult to identify the model of this system. The nonlinear relation of I/O data can be expressed by artificial neural network (ANN). The ANN can fit the any function accurately by studying. In this paper, the FCS of a type of fighter is identified by radial basis function (RBF) network. The training...
The development of accurate models to describe and predict pressure inactivation kinetics of microorganisms is very beneficial to the food industry for optimization of process conditions. The need for “intelligent” methods to model highly nonlinear systems is long established. Feed-forward neural networks have been successfully used for modeling of nonlinear systems. The objective of this research...
In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility...
The maze traversal problem involves finding the shortest distance to the goal from any position in a maze. Such maze solving problems have been an interesting challenge in computational intelligence. Previous work has shown that grid-to-grid neural networks such as the cellular simultaneous recurrent neural network (CSRN) can effectively solve simple maze traversing problems better than other iterative...
The power systems of the future - smart grid - will see an increase in both renewable energy sources and load demand increasing the need for fast dynamic reconfiguration of system parameters. Advanced simulation capabilities are needed to support future planning and to implement real-time dynamic optimization. To achieve this, intelligent algorithms are needed for prediction and system monitoring...
The novel Imperialist Competitive Algorithm (ICA) that was recently introduced has a good performance in some optimization problems. The ICA inspired by sociopolitical process of imperialistic competition of human being in the real world. In this paper, a new Adaptive Imperialist Competitive Algorithm (AICA) is proposed. In the proposed algorithm, for an effective search, the Absorption Policy changed...
Autonomous navigation and robust obstacle avoidance are prerequisites for the successful operation of a planetary rover. Typical approaches to tackling this problem rely on complex and computationally expensive navigation strategies based upon the creation of 3D maps of the environment. In contrast, this research proposes a simple artificial neural network relying on infrared sensory input as the...
In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications...
Based on the hypothesis that all intellectual activities of human beings are performed as optimization processes, we design Hopfield networks that can coordinately achieve robot planning. Planning is a typical intellectual activity of a logical task, which is considered difficult for neural networks to do. For an example of robot planning, we take the Warehouse Keeper Puzzle, called sokoban, the goal...
The paper discusses the quadratic neural unit (QNU) and highlights its attractiveness for industrial applications such as for plant modeling, control, and time series prediction. Linear systems are still often preferred in industrial control applications for their solvable and single solution nature and for the clarity to the most application engineers. Artificial neural networks are powerful cognitive...
In this paper we present the results of the first experiments in the investigation of automatically adjusting the learning parameters of an EFuNN. This work in part addresses previous work which speculated that this evolving connectionist system could be further developed with a view to either reducing the overall number of learning parameters or having them adjusted automatically. One of these areas...
In the flat steel cold rolling process, real-time controllers get their reference values (setpoints) using a mathematical model. Such a model is carried out at the process optimization level of the plant automation architecture. Since not all variables needed by the model can be effectively measured, and since a very accurate modeling would be unsuitable for real-time application or unachievable at...
The design of radial basis function widths of Radial Basis Function Neural Network (RBFNN) is thoroughly studied in this paper. Firstly, the influence of the widths on performance of RBFNN is illustrated with three simple function approximation experiments. Based on the conclusions drawn from the experiments, we find that two key factors including the spatial distribution of the training data set...
The wavelet analysis and neural network for fault diagnosis system in practice has become a hot research topic in the fields of pattern recognition system in recent years. Acoustic emission technology is used for vibrating screen's fault diagnosis in this paper. The energy feature vectors of signals extracted by use of wavelet packet analysis is regarded as neural network input vectors, and the system...
The combinatorial optimization occurs in many real-world problems including the fields of engineering, physics and economics. It has been recognized that some problems with highly degenerate states are difficult to solve in terms of many existing optimization algorithms. This paper proposes a novel stochastic method with modified extremal optimization (EO) and nearest neighbor search to deal with...
In process system engineering field, plant-wide optimization becomes an important research issue in relation to model based control strategy and software aided solution integration. A platform with the function of simulation, data analysis, and fault diagnosis for operation system optimal control is proposed. The function of five levels in the proposed platform is discussed. Web services technology...
In this paper we used a generalized net which gives a possibility for parallel optimization of multilayer neural networks. For training the backpropagation algorithm with momentum was considered. We proposed a generalized net model of parallel training of two neural networks with different architectures. The difference between the networks is in the number of neurons in main difference of the neural...
The collection vehicle routing problems with intermediate facilities (CVRP-IF) is actually belong to a well-known generalization of VRP, the Multi-Depot Vehicle Routing Problem with Inter-Depot Routes (MDVRPI), which is a combinatorial optimization problem and holds a central place in reverse logistics management, such as waste collection management. This paper presents an improved multiple ant colony...
Neural network based models are developed and used for the description of the relations of the geometry characteristics of Steel 3 welds from orbital arc welding (OAW) process parameters. This integrated methodology is implemented together with response surface methodology (statistical approach) for the investigation of the defined as quality characteristics: outer and inner weld widths. Both implemented...
Under the research context of two axis digital control turntable, this paper has set up a mathematical model of control system of turntables. Proceeding from such model, it has made an analysis on the deficiencies of classical PID control during turntable control. Against the feature of PID parameters difficult to be tuned, the neural network is thus introduced in the optimizing process of turncontrol...
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