Synergies and partnerships

McInerney, Thomas F.

doi:10.1017/cbo9781316105757.007

Cited by 1 publication

(1 citation statement)

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“…As already discussed, the learning schemes followed by Backpropagation or the Resilient Backpropagation, based on the gradient descent methods, have several limitations. In these gradient-based algorithms it is difficult to obtain a unique set of optimal parameters due to the existence of multiple local optima [17]. The presence of these local minima hampers the search for global minimum because these algorithms frequently get trapped in local minima regions and hence incorrectly identify local minimum as the global minimum.…”

Section: Ann Methodology and Brief Description Of Algorithms Usedmentioning

confidence: 99%

Comparative performance of some popular artificial neural network algorithms on benchmark and function approximation problems

et al. 2010

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Abstract. We report an inter-comparison of some popular algorithms within the artificial neural network domain ( viz., Local search algorithms, global search algorithms, higher order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-Bit parity and Two Spiral. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg-Marquardt algorithm yields the lowest RMS error for the N-bit Parity and the Two Spiral problems, Higher Order Neurons algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the Neuro Fuzzy algorithm. The above algorithms were also applied for solving several regression problems such as cos(x) and a few special functions like the Gamma function, the complimentary Error function and the upper tail cumulative χ 2 -distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg-Marquardt algorithm yields the best results. Keeping in view the highly nonlinear behaviour and the wide dynamic range of these functions, it is suggested that these functions can be also considered as standard benchmark problems for function approximation using artificial neural networks.

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Section: Ann Methodology and Brief Description Of Algorithms Usedmentioning

confidence: 99%