Smooth Function Approximation Using Neural Networks

Ferrari, Silvia; Stengel, Robert F.

doi:10.1109/tnn.2004.836233

Cited by 288 publications

(131 citation statements)

References 25 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…To derive a meta-model from the original high-complexity model by machine learning technique it is necessary to generate sufficient training and validation examples from the original model. SVM (Vapnik, Golowich & Smola, 1997) and ANN (Ferrari & Stengel, 2005) can be efficiently used for the approximation of multidimensional nonlinear functions.…”

Section: Sopnn and Mlp Models For Time-constrained Applicationsmentioning

confidence: 99%

Optimization of self-organizing polynomial neural networks

Marić

2013

Expert Systems with Applications

View full text Add to dashboard Cite

The main disadvantage of self-organizing polynomial neural networks (SOPNN) automatically structured and trained by the group method of data handling (GMDH) algorithm is a partial optimization of model weights as the GMDH algorithm optimizes only the weights of the topmost (output) node. In order to estimate to what extent the approximation accuracy of the obtained model can be improved the particle swarm optimization (PSO) has been used for the optimization of weights of all node-polynomials. Since the PSO is generally computationally expensive and time consuming a more efficient Levenberg-Marquardt (LM) algorithm is adapted for the optimization of the SOPNN. After it has been optimized by the LM algorithm the SOPNN outperformed the corresponding models based on artificial neural networks (ANN) and support vector method (SVM). The research is based on the meta-modeling of the thermodynamic effects in fluid flow measurements with time-constraints. The outstanding characteristics of the optimized SOPNN models are also demonstrated in learning the recurrence relations of multiple superimposed oscillations (MSO). KeywordsPolynomial neural networks GMDH Levenberg-Marquardt algorithm Particle swarm optimization Time series modeling 3

show abstract

Section: Sopnn and Mlp Models For Time-constrained Applicationsmentioning

confidence: 99%

Optimization of self-organizing polynomial neural networks

Marić

2013

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…A classical manner in which one may constructf (x) is by adopting a neural network formalism ( [17]). In particular, let…”

Section: B Neural-based Functional Approximationmentioning

confidence: 99%

Estimation of relative position and coordination of mobile underwater robotic platforms through electric sensing

Morel

Porez²,

Ijspeert

2012

2012 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

To cite this version:Yannick Estimation of Relative Position and Coordination of Mobile Underwater Robotic Platforms through Electric SensingYannick Morel, Mathieu Porez, and Auke J. IjspeertAbstract-In the context of underwater robotics, positioning and coordination of mobile agents can prove a challenging problem. To address this issue, we propose the use of electric sensing, with a technique inspired by weakly electric fishes. In particular, the approach relies on one or several of the agents applying an electric field to their environment. Using electric measures, others agents are able to reconstruct their relative position with respect to the emitter, over a range that is function of the geometry of the emitting agent and of the power applied to the environment. Efficacy of the technique is illustrated using a number of numerical examples. The approach is shown to allow coordination of unmanned underwater vehicles, including that of bio-inspired swimming robotic platforms.

show abstract

“…Currently, the common fitness estimation methods include the fitness inheritance and the application of surrogate model [11][12][13][14][15][16][17]. However, which method will perform better in fitness estimation?…”

Section: Introductionmentioning

confidence: 99%

Efficient Query for Historical Data in Evolutionary Algorithm

Tian¹,

Pan²,

Huang³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

View full text Add to dashboard Cite

Abstract-For the time-consuming problem in calculating the fitness value, this paper proposes a hash bucket with precision mechanism for a quick query of the data in the neighborhood of a particle. In order to establish a balance between the calculation accuracy and utility, it uses the hash tables with precision mechanism to solve the problem in the storage and query of historical calculation data so that the neighborhood of a to-be-evaluated individual can be determined more accurately to reduce the error in estimating the fitness value. Moreover, it uses the typical reference functions to separately test the effectiveness and accuracy of the algorithms based on the values obtained in different dimensions. The test result proves that compared with the other algorithms described in this paper, our algorithm can provide a better solution in the context of the same number of times for fitness calculation.

show abstract

Smooth Function Approximation Using Neural Networks

Cited by 288 publications

References 25 publications

Optimization of self-organizing polynomial neural networks

Optimization of self-organizing polynomial neural networks

Estimation of relative position and coordination of mobile underwater robotic platforms through electric sensing

Efficient Query for Historical Data in Evolutionary Algorithm

Contact Info

Product

Resources

About