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Neural networks have been applied very successfully in the identification and control of nonlinear dynamic systems. The paper presents a design of neural network based control system for 2DOF nonlinear laboratory helicopter model (Humusoft CE 150). The main objective of this paper is to develop artificial neural networks to control helicopter's motors, or consequently elevation and azimuth angles...
Invasive Weed Optimization Algorithm IWO) is an ecologically inspired metaheuristic that mimics the process of weeds colonization and distribution and is capable of solving multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. In this article a modified version of IWO has been used for training the feed-forward Artificial Neural Networks (ANNs) by adjusting the...
An on-line tracking controller design based on using T-S fuzzy-neural modeling for a class of general robot manipulators is investigated in this paper. Also, we use projection update laws to tune adjustable parameters for preventing parameters drift. In addition, stability of the closed-loop systems is proven by using strictly-positive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees...
The centered OWA (C-OWA) operator weights, defined as the center point of a convex hull constructed by the extreme points of ordinal relation, are computed by coordinate-wise averaging of the extreme points. The resulting C-OWA operator weights display some interesting properties. First there exists one to one correspondence between the multiplier used in the ordinal relation and the attitudinal character,...
The problem treated in this paper is about optimal visual sensors placement and deployment with estimation of the appropriate location of an interceptor to be placed. The main objective of this research is to ensure accurate coverage of the monitoring space with a minimum number of directional “field-of-view” (FOV) cameras and in the same time to decrease the interception time of an intruder at any...
The purpose of this paper is to study how to improve the evolution of GNP-Sarsa with subroutines and its application to trading rules on stock markets. Recently, a successful study, namely GNP-Sarsa, shows us its effectiveness and powerfulness, which combines sophisticated diversified search ability for structures using evolution and intensified search ability of RL for many technical indices and...
In this paper, radial basis function network (RBFN) with sliding-mode controller (SMC) is designed to the joint position control of two-link robot manipulators for periodic motion and predefined trajectory tracking control. Radial basis function uses curve fitting mode to obtain the nonlinear mapping. The unavoidable learning procedure degrades its transient performance in the existence of disturbance...
Subject construction is a fundamental construction in the universities, and it is also the pioneer of reform and development. Nowadays through the subject construction, it is a pressing strategic task for universities to promote the overall educational level and the scientific research capacity to become one of the famous and world-class universities. In this article I gave an introduction to the...
Highly-functional mobile devices, such a smart phone, have appeared. Location based services of the mobile devices are assimilated in a variety of ways. Then, Indoor localization sensor is necessary to access the location based services seamlessly. This paper researched the performance of indoor localization with ZigBee based particle filter. This paper showed this method can localize a resting target...
Centrifugal chiller plants (CCP) are widely used in air conditioning systems, its operation optimization can save lots of energy and has great significance in environmental protection. The optimization is a large-scale nonlinear problem and there is no practical algorithm until now. This paper proposes a new method to do this operation optimization using continuous piecewise linear programming (CPWLP)...
In this paper, we investigate the performance and dependability modeling of voice and data services in computer networks. We use Stochastic Petri Net as an enabling modeling approach for analytical evaluation of complex scenarios. Our goal is to analyze the performability of a network infrastructure, by considering the influence of queuing policies and network topologies on the quality of voice and...
This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to a pursuit-evasion differential game in which both the pursuer and...
Type-2 fuzzy sets minimize the effects of uncertainties that cannot be modeled using type-1 fuzzy sets. However, the computational complexity of the type-2 fuzzy sets is very large and it is more difficult to use and understand than are type-1 fuzzy sets. This paper proposes sine-square embedded fuzzy sets and gives a comparison with type-2 and nonstationary fuzzy sets. The sine-square embedded fuzzy...
This study aimed to identify the variables that most influence the potable water consumption of the State of Paraná, Brazil. The study attempted to model the consumption of water using Artificial Neural Networks (ANN) associated with the extraction of knowledge. The results indicate that the water consumption of the State of Paraná is directly related to socio-environmental factors. However, when...
This paper proposes a method to fuse Real-valued K nearest neighbor classifier by feature grouping. Real-valued K nearest neighbor classifier can approximate continuous-valued target functions, which can provide more information than crisp K nearest neighbor classifier in fusion. In addition real-valued K nearest neighbor classifier is sensitive to feature perturbation. Therefore, when multiple real-valued...
In this paper, a hybrid structure is proposed to simulate thermal behaviors of pools. The structure uses several neural models to represent the climatic data and a parametric estimation algorithm to determine the variation in volume due to the human activity. The new structure allows adapting the thermal dynamic model for pools, considering variations over time for different regional weather conditions...
An approach to accelerating the learning process of the actor-critic learning algorithm for reinforcement learning is presented. The algorithm was derived from principles based on the prediction of average rewards and temporal difference (TD) learning with averaged and discounted rewards. The derived algorithm was applied to neural networks, demonstrating their effective operation in nonlinear control...
This paper presents a new system that reconstructs and visualizes 3D Purkinje cells (neurons) from two-photon microscopy images. The main components of the system are nonlinear diffusion filtering for denoising of each two-photon microscopy slice, increasing the image resolution of each Purkinje cell slice, global image enhancement of each slice as well as local pixel based image enhancement of each...
For some difficult problems, artificial neural network (ANN) ensemble classifiers, instead of a single ANN classifier, are considered. The ensemble usually has better generalization performance than any individual network for classification problems. But, it is not easy to construct the ANN ensemble. In the previous study, the authors presented the systematic trajectory search algorithm (STSA) to...
This study examines the price estimation capability of MAIS (Multi-Agent Intelligent Simulator) when two types of agents with different learning capabilities coexist in a power trading market. This study identifies that the proposed MAIS, considering the coexistence of different types of agents, can improve its estimation accuracy of wholesale electricity price. This study also reexamines the estimation...
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