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In this paper, a new iterative algorithm for determination of direction cosine matrix (DCM) is presented, it can be expressed as two forms, matrix exponential and six scalar differential equations respectively, corresponding to each form, related algorithms used for the orthonormalization are derived, although the simulations and algorithm analysis are omitted here, the Van test results verified its...
An approach based on chaos theory and fuzzy neural network (FNN) is proposed for chaotic time series prediction. Firstly, C-C algorithm is applied to estimate the delay time of chaotic signal. Grassberger-Procaccia (G-P) algorithm and least squares regression are employed to calculate the correlation dimension of chaotic signal simultaneously. Considering the difficulty in determining the number of...
We give the first improvement to the space/approximation trade-off of distance oracles since the seminal result of Thorup and Zwick [STOC'01]. For unweighted graphs, our distance oracle has size O(n5/3) = O(n1.66⋯) and, when queried about vertices at distance d, returns a path of length 2d + 1. For weighted graphs with m = n2/α edges, our distance oracle has size O(n2/3√α) and returns a factor 2 approximation...
A hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for Video OCR is presented in this paper. Video OCR is an important task towards enabling automatic content-based retrieval of digital video databases. However, since text is often displayed against a complex background, its detection and extraction is a challenging problem. In this paper, wavelet transformation is done...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
This paper describes a novel fast mean shift algorithm based on a resampling technique with marked regular pyramid structure. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By resampling the original image with marked regular pyramid structure, improved method reduces the number of pixels requiring...
The problem of environmental quality assessment is a pattern recognition problem, and a well-trained ANN can exploit the underlying nonlinear relationships that determine the environmental rating of a region. In this study, we are trying with the neural network model to make an effective analysis for environmental quality assessment. A 4-9-1 three-layer feedforward neural network using the backpropagation...
A new network for fuzzy-neural system was proposed based on the analysis and comparison of existing methods, which could be easy to distill the fuzzy rules. The network structure was adjusted by FBP(Fuzzy Back Propagation) learning algorithm to acquire network parameters and variable weights. By aiming at disadvantage of IP algorithm on rule-optimization, the Improved Iterative Pruning Neural Network...
This paper deals with a sliding mode impedance control (SMIC) of end-effector of robot manipulator using a tracking control scheme and a real-time artificial intelligence algorithm based on a radial basis function neural networks(RBFNNs). To real-time estimate the design parameters of desired impedance model such as desired inertia, damping, and stiffness desired impedance, SMIC(sliding mode impedance...
The energy efficiency in wireless sensor networks plays a very important role because of the limited power of the battery used within nodes. This paper presents efficient energy aware routing (EEAR) algorithm for wireless sensor networks, that makes routing decisions on the basis of node's residual energy. EEAR explores the optimized routing path by implementing a model which is function of the shortest...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
An optimal adaptive controller for multidimensional disturbed systems is concerned in this paper. The system parameters, especially the first parameters of the controller, are unknown a prior. The one-step-ahead adaptive controller is designed based on the input matching technique and the extended least-squares (ELS) algorithm. It is shown that the system identification is consistent and the adaptive...
Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have...
Networks on chip (NoC), a new packet-based design method, with a new dependable no deadlock (DND) back-tracking routing algorithm are proposed to implement artificial neural network (ANN). This system is simulated by NIRGAM NoC simulator to get system performance. Experimental results show that this proposed system has higher connection-per-second (CPS), lower communication load than the exiting other...
This paper presents the designed obstacle avoidance program for mobile robot that incorporates a neuro-fuzzy algorithm using Altera?? Field Programmable Gate Array (FPGA) development DE2 board. The neuro-fuzzy-based-obstacle avoidance program is simulated and implemented on the hardware system using Altera Quartus?? II design software, System-on-programmable-chip (SOPC) Builder, Nios?? II Integrated...
In this paper, the feasibility of using probabilistic causal-effect model is studied and we apply it in particle swarm optimization algorithm (PSO) to classify the faults of mine hoist. In order to enhance the PSO performance, we propose the probability function to nonlinearly map the data into a feature space in probabilistic causal-effect model, and with it, fault diagnosis is simplified into optimization...
A kind of adaptive PID control algorithm is analyzed, and the drawbacks of the existing algorithms are commented. As an improvement, a neural network intelligent control algorithm based on one-step prediction is developed. Result show that the new control method is more adaptable to the control of time-varying and nonlinear control systems.
As a crucial procedure in which images are divided into distinct non-overlapping regions and the interested objects are extracted in the process of image analyzing and image recognizing, image segmentation plays a considerable role in extracting medical lesions, measuring specific tissues, and realizing three-dimensional reconstruction. In this paper, integrated with the studies of dental micro-CT...
Greenhouse environment models easily fitted strong noise data, and its' generalization decreased. In this paper, ROLS (Regularized Orthogonal Least Squares) algorithm effectively decreased the influence of noise data, and automatically designed smaller NN structure. PSO (Particle Swarm Optimization) algorithm optimized the parameters of model. Model was experimented with spring environment data of...
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