Dynamic Multi-Response Experiments by Backpropagation Networks and Desirability Functions

Chang, Hsu-Hwa

doi:10.1080/10170660609509324

Cited by 17 publications

(28 citation statements)

References 22 publications

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“…Realizing the need of the modern industries, some authors took research interest in developing appropriate procedure for optimizing multi-response dynamic systems. Different researchers have advocated different approaches for modelling the multiple responses but most of them (Tong et al, 2001;Hsieh et al, 2004;Chang, 2006;Chang, 2008;Wu, 2009;Chang and Chen, 2011) have used CDF as the performance metric for optimization of the multi-response dynamic systems. Tong et al (2004), Wang and Tong (2004), Wu and Yeh (2005), Wang (2007) and Gauri (2014) have used different performance metrics.…”

Section: The Approaches For Optimizing a Multi-response Dynamic Systemmentioning

confidence: 99%

“…Chang (2008) illustrated application of his proposed data mining approach for optimizing multi-response dynamic systems using a case study adopted from Chang (2006 Y is DSTB type variable. In this case, six control factors A -F, each at three levels (1, 2, and 3), were considered and arranged in a standard 18 L orthogonal array as shown in Table 1.…”

Section: The Experimental Datamentioning

confidence: 99%

“…Industry has increasingly emphasized developing procedures capable of simultaneously optimizing the multi-response dynamic systems in light of the increasing complexity of modern product design. To cope with the need of the modern industries, several studies (Tong et al, 2001;Tong et al, 2004;Hsieh et al, 2005;Wu & Yeh, 2005;Chang, 2006;Wang, 2007;Chang, 2008;Tong et al, 2008;Wu, 2009;Chang & Chen, 2011;Gauri, 2014) have proposed different procedures for optimizing a multi-response dynamic system. The goal of optimizing a multi-response dynamic system is to find a setting combination of control factors (controllable variables) that would result in the optimum values of all response variables at all signal levels.…”

Section: Introductionmentioning

confidence: 99%

“…Tong et al (2001), Hsieh et al (2005), and Wu (2009) have modelled the response variables using response surface methodology (RSM) and then determined the optimal settings of the control factors by maximizing an overall performance measure (OPI), which is essentially the CDF. On the other hand, Chang (2006), Chang (2008) and Chang and Chen (2011) used artificial neural networks (ANN) for modelling the response functions and then obtained the optimal settings of the control factors by considering OPI, which is essentially CDF, as the performance metric.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Gauri¹,

Pal²

2017

10.5267/j.ijiec

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A dynamic system differs from a static system in that it contains signal factor and the target value depends on the level of the signal factor set by the system operator. The aim of optimizing a multi-response dynamic system is to find a setting combination of input controllable factors that would result in optimum values of all response variables at all signal levels. The most commonly used performance metric for optimizing a multi-response dynamic system is the composite desirability function (CDF). The advantage of using CDF is that it is a simple unit less measure and it has a good foundation in statistical practice. However, the problem with the CDF is that it does not consider the variability of the individual response variables. Moreover, if the specification limits for the response variables are not provided the CDF cannot be computed. In this paper, a new performance metric for multi-response dynamic system, called multiple regression-based weighted signal-to-noise ratio (MRWSN) is proposed, which overcome the limitations of CDF. Two sets of experimental data on multi-response dynamic systems, taken from literature, are analysed using both CDF-based and the proposed MRWSN-based approaches for optimization. The results show that the MRWSN-based approach also results in substantially better optimization performance than the CDF-based approach.

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Section: The Approaches For Optimizing a Multi-response Dynamic Systemmentioning

confidence: 99%

Section: The Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Gauri¹,

Pal²

2017

10.5267/j.ijiec

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show abstract

“…Su et al [21] applied artificial neural networks (ANN) to model the relation of controllable factors and responses, and the used scatter search (SS) to find best setting in a problem with dynamic characteristics. Chang [22] also used ANN to build the dynamic response model and proposed modified desirability functions for optimizing phase. Chang [23] proposed a data mining approach which uses artificial neural networks, desirability functions, and simulated annealing algorithm to optimize problems with dynamic multiple responses.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

2017

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In some processes, quality of a product should be characterized by functional relationships between response variables and a signal factor. Hence the traditional methods cannot be used to find the optimum solution. In this paper, we propose a method which considers two different dispersion effects, i.e. in domain and between replicates variations in the functional responses. Besides, we propose an integral based measure to find the deviation from target function. A probabilistic method is applied to consider the correlation structure of functional responses. Three numerical examples and a real case from literature are studied to show the efficiency of the proposed method.

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A Review of Analysis of Dynamic Response in Design of Experiments

Panpan

Zhang

et al. 2014

Quality & Reliability Eng

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Systems with dynamic characteristics have gained increasing attention of both researchers and engineers in industry in recent years. The purpose of this article is to provide an overview of investigations on what might be called dynamic response in experimental design, so as to facilitate further research in this area. We briefly review the development of research on dynamic problems in diverse fields such as statistical quality control, biostatistics, experimental design and repeated measurements. Discussions in the past are specially sorted out on the definition and classification of dynamic response problems as encountered in general experimental design and robust design, and various perspectives of system types and response types. Four categories of modelling techniques in classical experimental design are summarized from the literature according to distinct application conditions; this would provide a useful basis for future investigations. Copyright © 2014 John Wiley & Sons, Ltd.

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Dynamic Multi-Response Experiments by Backpropagation Networks and Desirability Functions

Cited by 17 publications

References 22 publications

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Optimization of multi-response dynamic systems using multiple regression-based weighted signal-to-noise ratio

Optimization of Multi-response Problems with Continuous Functional Responses by considering dispersion effects

A Review of Analysis of Dynamic Response in Design of Experiments

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