Automated Fault Detection and Diagnosis for an Air Handling Unit Based on a GA-Trained RBF Network

Huang, Yonghong; Li, Nianping; Shi, Yangchun

doi:10.1109/icccas.2006.285078

Cited by 3 publications

(1 citation statement)

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“…An increasing amount of literature is available on RBFNs methods which has became a popular technique since the 1980s because of their simple structure, well established theoretical basis and fast learning speed, which are all crucial factors in real applications [6]. Due to the popularity of RBF neural networks, several researchers have been working during the last decade to develop more effcient training algorithms, compared to the standard techniques [7], such as two-phase approach [8]; Online RBFNs self-generate [9]; Growing strategies [10]; Pruning methods [11]; Evolutionary computation techniques [12]. These algorithms have promoted the development of RBFNs training method at a certain extent; however these methods did not completely resolve the structure and parameters optimization.…”

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confidence: 99%

A Multi-Coding GA-BP-RBF Model for China Human Capital Prediction

Zhu

Liu

2009

2009 Second International Conference on Intelligent Computation Technology and Automation

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Human capital formation and accelerating economic growth is a representative complex system which is not suitable to measure and forecast by classic linear statistical approaches. This paper proposes a novel hybrid multi-coding GA-BP-RBF model for China human capital prediction. The hybrid model combines the multi-encoding genetic algorithm (MGA) and the back propagation (BP) algorithm to form a hybrid learning algorithm (MGA-BP) for training Radial Basis Function Networks (RBFNs). The human capital prediction results obtained are compared with existent bibliography, showing an improvement over the published methods.

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confidence: 99%

A Multi-Coding GA-BP-RBF Model for China Human Capital Prediction

Zhu

Liu

2009

2009 Second International Conference on Intelligent Computation Technology and Automation

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A hybrid MGA-BP algorithm for RBFNs self-generate

Zhu

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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Calculating the Moore–Penrose Generalized Inverse on Massively Parallel Systems

et al. 2022

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In this work, we consider the problem of calculating the generalized Moore–Penrose inverse, which is essential in many applications of graph theory. We propose an algorithm for the massively parallel systems based on the recursive algorithm for the generalized Moore–Penrose inverse, the generalized Cholesky factorization, and Strassen’s matrix inversion algorithm. Computational experiments with our new algorithm based on a parallel computing architecture known as the Compute Unified Device Architecture (CUDA) on a graphic processing unit (GPU) show the significant advantages of using GPU for large matrices (with millions of elements) in comparison with the CPU implementation from the OpenCV library (Intel, Santa Clara, CA, USA).

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Automated Fault Detection and Diagnosis for an Air Handling Unit Based on a GA-Trained RBF Network

Cited by 3 publications

References 1 publication

A Multi-Coding GA-BP-RBF Model for China Human Capital Prediction

A Multi-Coding GA-BP-RBF Model for China Human Capital Prediction

A hybrid MGA-BP algorithm for RBFNs self-generate

Calculating the Moore–Penrose Generalized Inverse on Massively Parallel Systems

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