Artificial Intelligence and Integrated Intelligent Systems in Product Design and Development

Zha, Xuan F.

doi:10.1007/978-1-4020-7829-3_32

Cited by 6 publications

(5 citation statements)

References 109 publications

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“…The basic form is shown in figure 5. Using the identification method is similar to a geometric fuzzy approach, through the calculation of j D and ( , ) R i j of each fault similarity vector j R to determine the possibility (D , ) [8]. This system adopts the maximum, minimum method to calculate the fuzzy nearness degree.…”

Section: Design and Implementation Of Virtual Instrument Software Panelmentioning

confidence: 99%

The Research of Intelligent Fault Diagnostic System for Diesel Engine Based on Virtual Instrument

Hu¹,

Qian²,

Chen³

2014

AMM

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Introduced the system structure, working principle and diagnostic algorithm of a data acquisition and fault diagnostic system for diesel engines. The system is composed of a portable computer, a multi-channels data acquisition device and some sensors. Through behavior of the diesel and the characteristic values of signals, we can finish the non-disassemble examining and fault diagnosing. The structure and policy of the diagnostic expert system are also discussed.

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Section: Design and Implementation Of Virtual Instrument Software Panelmentioning

confidence: 99%

The Research of Intelligent Fault Diagnostic System for Diesel Engine Based on Virtual Instrument

Hu¹,

Qian²,

Chen³

2014

AMM

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“…Since 1993 NNs have been used in certain classes of optimal design problems [4][5][6], in the automation design processes [7,8], in retrieval processes, simulations, decision making, pattern recognition and prediction [9][10][11][12][13][14][15], including some recent contributions [16,17,18]. In addition to these [19][20][21] are very good sources where the latest application of artificial intelligence and integrated intelligent systems for concurrent integration and collaboration of the design of a product and its related processes are presented.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Neural Network Models in Engineering Design

Stamov¹

2020

IJEAT

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In this paper neural networks applications in engineering design are discussed. The question for stability of their steady states is also considered. Some new efficient criteria are proposed. Since neural networks are relevant systems applied in various engineering design tasks, including many optimization and control problems, the results can be useful in design of such systems of diverse interest.

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“…Automatic design, analysis, evaluation, modification, and optimization of designed parameters are important issues to be addressed in the design process. There are many individual intelligent approaches such as expert systems, fuzzy logic, neural networks, and genetic algorithms that can accomplish these tasks [10,41,46]. However, due to particular computational properties of individual intelligent system techniques, hybrid solutions in the intelligent systems community are required to solve complex design problems, which may integrate one or more of the mentioned individual intelligent techniques [13,30,45,46,47].…”

Section: Introductionmentioning

confidence: 99%

Soft computing in engineering design: a hybrid dual cross-mapping neural network model

Zha

2005

Neural Comput & Applic

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Contemporary design process requires the development of a new computational intelligence or soft computing methodology that involves intelligence integration and hybrid intelligent systems for design, analysis and evaluation, and optimization. This paper first presents a discussion of the need to incorporate ''intelligence'' into an automated design process and the various constraints that designers face when embarking on industrial design projects. Then, it presents the design problem as optimizing the design output against constraints and the use of soft computing and hybrid intelligent systems techniques. In this paper, a softcomputing-integrated intelligent design framework is developed. A hybrid dual cross-mapping neural network (HDCMNN) model is proposed using the hybrid soft computing technique based on ''cross-mapping'' between a back-propagation network (BPNN) and a recurrent Hopfield network (HNN) for supporting modeling, analysis and evaluation, and optimization tasks in the design process. The two networks perform different but complementary tasks-the BPNN ''decides'' if the design problem is a ''type 0'' (rational) or ''type 1'' (non-rational) problem, and the output layer weights are then used as the energy function for the HNN. The BPNN is used for representing design patterns, training classification boundaries, and outputting network weight values to the HNN, and then the HNN uses the calculated network weight values to evaluate and modify or re-design the design patterns. The developed system provides a unified soft-computingintegrated intelligent design framework with both symbolic and computational intelligence. The system has self-modifying and self-learning functions. Within the system, only one network training is needed for accomplishing the evaluation, rectification/modification, and optimization tasks in the design process. Finally, two case studies are provided to illustrate and validate the developed model and system.

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Artificial Intelligence and Integrated Intelligent Systems in Product Design and Development

Cited by 6 publications

References 109 publications

The Research of Intelligent Fault Diagnostic System for Diesel Engine Based on Virtual Instrument

The Research of Intelligent Fault Diagnostic System for Diesel Engine Based on Virtual Instrument

Stability Analysis of Neural Network Models in Engineering Design

Soft computing in engineering design: a hybrid dual cross-mapping neural network model

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