A recursive algorithm for optimizing the architecture of feedforward neural networks by the stepwise addition of a reasonable number of hidden nodes is proposed. The recursive algorithm retains the calculation results...
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A recursive algorithm for optimizing the architecture of feedforward neural networks by the stepwise addition of a reasonable number of hidden nodes is proposed. The recursive algorithm retains the calculation results and approximation precision already obtained in the previous iteration step and uses them in the next step to efficiently lighten the computational burden of network optimization and training. The commonly used genetic algorithm has been modified for network training to circumvent the local optimum problem. Some new genetic operators, competition and self-reproduction, have been introduced and used together with some substantially modified genetic operators, crossover and mutation, to form a modified genetic algorithm (MGA) which ensures asymptotic convergence to the global optima with relatively high efficiency. The proposed methods have been successfully applied to concentration estimation in chemical analysis and quantitative structure-activity relationship studies of chemical compounds.
A heuristic recursive algorithm for the two-dimensional rectangular strip packing problem is presented. It is based on a recursive structure combined with branch-and-bound techniques. Several lengths are tried to dete...
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A heuristic recursive algorithm for the two-dimensional rectangular strip packing problem is presented. It is based on a recursive structure combined with branch-and-bound techniques. Several lengths are tried to determine the minimal plate length to hold all the items. Initially the plate is taken as a block. For the current block considered, the algorithm selects an item, puts it at the bottom-left corner of the block, and divides the unoccupied region into two smaller blocks with an orthogonal cut. The dividing cut is vertical if the block width is equal to the plate width;otherwise it is horizontal. Both lower and upper bounds are used to prune unpromising branches. The computational results on a class of benchmark problems indicate that the algorithm performs better than several recently published algorithms. (C) 2006 Elsevier Ltd. All rights reserved.
The multiple scattering problem can be solved using various analytical techniques. One of these techniques, the T-matrix formalism, is at the present time generally solved using iterative algorithms, because the initi...
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The multiple scattering problem can be solved using various analytical techniques. One of these techniques, the T-matrix formalism, is at the present time generally solved using iterative algorithms, because the initially proposed recursive algorithms appeared to be numerically unstable. We present here a new set of recursive relations to solve the multiple scattering equation, and discuss their range of application. In order to validate this new formalism, we compare numerical results for various complex systems with the Generalized Multi-particle Mie solution. We show that the results obtained with the recursive method are in very good agreement with those given by iterative techniques. (C) 2003 Elsevier Science Ltd. All rights reserved.
In this paper, we focus on the modeling problem of the multi-frequency signals which contain many different frequency components. Based on the Newton search and the measured data, a Newton recursive parameter estimati...
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In this paper, we focus on the modeling problem of the multi-frequency signals which contain many different frequency components. Based on the Newton search and the measured data, a Newton recursive parameter estimation algorithm is developed to estimate the amplitude, the angular frequency and the phase of a multi-frequency signal. In order to improve the performance of the identification algorithm, a convergence factor is introduced in the Hessian matrix of the developed Newton recursive method. The numerical examples verify that the proposed algorithm is effective for modeling the multi-frequency sine signals.
This study develops a novel recursive algorithm to significantly enhance the computation efficiency of a recently proposed stochastic subspace identification (SSI) methodology based on an alternative stabilization dia...
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This study develops a novel recursive algorithm to significantly enhance the computation efficiency of a recently proposed stochastic subspace identification (SSI) methodology based on an alternative stabilization diagram. Exemplified by the measurements taken from the two investigated office buildings, it is first demonstrated that merely one sixth of computation time and one fifth of computer memory are required with the new recursive algorithm. Such a progress would enable the realization of on-line and almost real-time monitoring for these two steel framed structures. This recursive SSI algorithm is further applied to analyze 20 months of monitoring data and comprehensively assess the environmental effects. It is certified that the root-mean-square (RMS) response can be utilized as an excellent index to represent most of the environmental effects and its variation strongly correlates with that of the modal frequency. More detailed examination by comparing the monthly correlation coefficient discloses that larger variations in modal frequency induced by greater RMS responses would typically lead to a higher correlation.
A new recursive algorithm for adaptive Kalman filtering is proposed. The signal state-space model and its noise statistics are assumed to depend on an unknown parameter taking values in a subset [', '] of Rs. ...
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A new recursive algorithm for adaptive Kalman filtering is proposed. The signal state-space model and its noise statistics are assumed to depend on an unknown parameter taking values in a subset [', '] of Rs. The parameter is estimated recursively using the gradient of the innovation sequence of the Kalman filter. The unknown parameter is replaced by its current estimate in the Kalman-filtering algorithm. The asymptotic properties of the adaptive Kalman filter are discussed.
Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dime...
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Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems.
A recursive orthogonal least squares (ROLS) algorithm for multi-input, multi-output systems is developed in this paper and is applied to updating the weighting matrix of a radial basis function network. An illustrativ...
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A recursive orthogonal least squares (ROLS) algorithm for multi-input, multi-output systems is developed in this paper and is applied to updating the weighting matrix of a radial basis function network. An illustrative example is given, to demonstrate the effectiveness of the algorithm for eliminating the effects of ill-conditioning in the training data, in an application of neural modelling of a multi-variable chemical process. Comparisons with results from using standard least squares algorithms, in batch and recursive form, show that the ROLS algorithm can significantly improve the neural modelling accuracy. The ROLS algorithm can also be applied to a large data set with much lower requirements on computer memory than the batch OLS algorithm.
A novel fast recursive minimum mean square error, successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed....
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A novel fast recursive minimum mean square error, successive interference cancellation (MMSE-SIC) algorithm with optimal detection order for vertical Bell Laboratories layered space-time (V-BLAST) systems is proposed. In this algorithm, the MMSE filter matrices and the optimal detection order are successively computed from the previously obtained filter matrices according to simple recursive pseudoinverse formulas, so that the algorithmic complexity is reduced significantly, especially for the practical number of transmit/receive antennas.
The recursive least square is widely used in parameter identification. But if is easy to bring about the phenomena of parameters burst-off. A convergence analysis of a more stable identification algorithm-recursive da...
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The recursive least square is widely used in parameter identification. But if is easy to bring about the phenomena of parameters burst-off. A convergence analysis of a more stable identification algorithm-recursive damped least square is proposed. This is done by normalizing the measurement vector entering into the identification algorithm. rt is shown that the parametric distance converges to a zero mean random variable. It is also shown that under persistent excitation condition, the condition number of the adaptation gain matrix is bounded, and the variance of the parametric distance is bounded.
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