An interactive artificial neural networks approach to multiresponse optimization

Noorossana, Rassoul; Zadbood, Amineh; Zandi, Faramak; Noghondarian, Kazem

doi:10.1007/s00170-014-6276-7

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…The primary objective of this manuscript is to evaluate the GP usefulness for problems with stochastic responses developed in the RSM framework, where polynomial regression techniques are predominant, namely the OLS and SUR techniques . For this purpose, three multiresponse optimization problems with different characteristics were selected from the literature and solutions obtained with response models fitted with OLS and SUR techniques compared with those of GP model.…”

Section: Objective and Research Methodologymentioning

confidence: 99%

Section: Objective and Research Methodologymentioning

confidence: 99%

“…22 The primary objective of this manuscript is to evaluate the GP usefulness for problems with stochastic responses developed in the RSM framework, where polynomial regression techniques are predominant, namely the OLS and SUR techniques. 23 For this purpose, in Wiley Online Library 3. Multiresponse optimization in the response surface methodology framework: an overviewStatistically designed experiments are considered essential for rapid learning, reducing time to market, reducing costs, and improving quality (reliability, performance, robustness,…) of process and product so RSM has been increasingly used to solve real-life problems and is a widely research topic (for research directions, see Khuri and Mukhopadhyay 24 ).…”

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confidence: 99%

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Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

Costa

Lourenço

2015

Quality & Reliability Eng

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This paper explores the benefits of Gaussian process model as an alternative modeling technique for problems developed in the Response Surface Methodology framework. Three case studies with different type and number of responses were investigated, and the compromise solutions obtained with three modeling techniques were evaluated. Results provide evidences of the Gaussian process model usefulness for stochastic responses, namely, when responses are correlated.

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Section: Objective and Research Methodologymentioning

confidence: 99%

Section: Objective and Research Methodologymentioning

confidence: 99%

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confidence: 99%

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Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

Costa

Lourenço

2015

Quality & Reliability Eng

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“…Fitness. For each chromosome, according to the classification information carried on it, according to the idea of distance classification, the classification of each sample in the original data can be determined, and the distance between the sample and its category center (here is Euclidean distance) can also be determined [27,28]. After determining the classification of the sample, the sum of the distances within the class can be calculated:…”

Section: Determination Of Fitness Function and Selection Ofmentioning

confidence: 99%

Neural Network Optimization Method and Its Application in Information Processing

Wang

Fan

2021

Mathematical Problems in Engineering

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Neural network theory is the basis of massive information parallel processing and large-scale parallel computing. Neural network is not only a highly nonlinear dynamic system but also an adaptive organization system, which can be used to describe the intelligent behavior of cognition, decision-making, and control. The purpose of this paper is to explore the optimization method of neural network and its application in information processing. This paper uses the characteristic of SOM feature map neural network to preserve the topological order to estimate the direction of arrival of the array signal. For the estimation of the direction of arrival of single-source signals in array signal processing, this paper establishes a uniform linear array and arbitrary array models based on the distance difference vector to detect DOA. The relationship between the DDOA vector and the direction of arrival angle is regarded as a mapping from the DDOA space to the AOA space. For this mapping, through derivation and analysis, it is found that there is a similar topological distribution between the two variables of the sampled signal. In this paper, the network is trained by uniformly distributed simulated source signals, and then the trained network is used to perform AOA estimation effect tests on simulated noiseless signals, simulated Gaussian noise signals, and measured signals of sound sources in the lake. Neural network and multisignal classification algorithms are compared. This paper proposes a DOA estimation method using two-layer SOM neural network and theoretically verifies the reliability of the method. Experimental research shows that when the signal-to-noise ratio drops from 20 dB to 1 dB in the experiment with Gaussian noise, the absolute error of the AOA prediction is small and the fluctuation is not large, indicating that the prediction effect of the SOM network optimization method established in this paper does not vary. The signal-to-noise ratio drops and decreases, and it has a strong ability to adapt to noise.

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“…Genetic and hybrid algorithms have been proposed and widely illustrated (see literature as examples). Neural networks, simulated annealing, and particle swarm have also been used to solve real‐life problems in the RSM framework. For a review on the state of the art, special features, trends on the development of search algorithms, and systematic comparison of some local and global algorithms, the reader is referred to the literature, as examples.…”

Section: Literature Overviewmentioning

confidence: 99%

Multiresponse problems: desirability and other optimization approaches

Costa

Lourenço

2016

Journal of Chemometrics

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A diversity of multiresponse optimization methods has been introduced in the literature; however, their performance has not been thoroughly explored, and only a classical desirability‐based criterion has been commonly used. With the aim of contributing to help practitioners in selecting an effective criterion for solving multiresponse optimization problems developed under the response surface methodology framework, and thus to find compromise solutions that are technically and economically more favorable, the working ability of several easy‐to‐use criteria is evaluated and compared with that of a theoretically sound method. Four case studies with different numbers and types of responses are considered. Less‐sophisticated criteria were able to generate solutions similar to those generated by sophisticated methods, even when the objective is to depict the Pareto frontier in problems with conflicting responses. Two easy‐to‐use criteria that require less‐subjective information from the user yielded solutions similar to those of a classical desirability‐based criterion. Preference parameters range and increment impact on optimal solutions were also evaluated.

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An interactive artificial neural networks approach to multiresponse optimization

Cited by 12 publications

References 30 publications

Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

Neural Network Optimization Method and Its Application in Information Processing

Multiresponse problems: desirability and other optimization approaches

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