Particle Swarm Optimization Neural Network and Its Application in Soft-Sensing Modeling

Chen, Guochu; Yu, Junzhi

doi:10.1007/11539117_86

Cited by 20 publications

(11 citation statements)

References 3 publications

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“…Chen and Yu [21] 2005 Proposition of a soft-sensor based on neural network and particle swarm optimization and validating its performance for soft sensing of gasoline endpoint through a comparative analysis using traditionally design neural network based soft sensors Luo and Shao [22] 2006 Developing a soft sensor based on neuro-fuzzy system, rough set theory and genetic algorithm for online estimation of freezing point of the light diesel fuel in fluid catalytic cracking unit, and demonstrating its efficacy through a comprehensive comparative study Xu et al [23] 2006 Design and testing of a soft sensor based on artificial neural network trained by Kalman particle swarm optimization algorithm for real-time monitoring of Acrylonitrile yield, and demonstrating its performance by conducting a comparative analysis Delgado et al [24] 2009 Designing a neuro-coevolutionary genetic fuzzy soft sensor for inferring the product composition in petroleum refining process, and proving its efficiency through a comprehensive comparative analysis Lahiri and Khalfe [25] 2010 Design and evaluation of a soft sensor based on support vector regression trained by differential evolutionary algorithm for online monitoring of commercial petrochemical plant and demonstrating its efficacy for the considered case study Sun and Ma [26] 2012 Designing a robust soft sensor using a fuzzy inference system optimized by genetic algorithm and particle swarm optimization methods, and proving its high computational capability using some traditional soft sensor designing techniques…”

Section: Time-delayed Dynamic Neural Networkmentioning

confidence: 98%

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the realtime behavior of the engine over coldstart periods.Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bioinspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

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Section: Time-delayed Dynamic Neural Networkmentioning

confidence: 98%

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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“…A Gaussian function used as the basis function and method of formula and coincidence degree algorithm used for the selection of input variables. In [62], studied the development procedure of PSO-NN soft-sensing model. In [63], the authors proposed the robust NN based soft-sensing modeling method for the biomass concentration in the process of fermentation.…”

Section: Neural Network-based Soft-sensing Modelsmentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

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“…Besides, it is computationally inexpensive in terms of both memory requirements and speed. As an evolutionary computation algorithm, PSO is an attractive choice for nonlinear programming because of the characteristics mentioned above [3][4][5]. But, it also has some defects, such as its global model is easily to fall into local optimum, and local model has disadvantage of slowest convergence in the later stage of evolution period during solving some complex problems.…”

Section: Introductionmentioning

confidence: 99%

Simplified particle swarm optimization algorithm based on particles classification

Chen

2010

2010 Sixth International Conference on Natural Computation

Self Cite

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When basic particle swarm optimization algorithm (PSO) is used to resolve some complex problems, its global optimal model usually falls into local optimal value and its local model has slowest convergence velocity in the later stage of evolution. So, a simplified particle swarm optimization algorithm is proposed. Firstly, all particles in whole swarm are divided into three categories, denoted as the better particles, the ordinary particles and the worse particles according to their fitness. After the velocity equation of PSO is analyzed, the velocity part of PSO's iteration equations is removed rationally. Then, these three types of particles evolve dynamically according to three corresponding kinds of simplified algorithm models. Then, PSO, other two improved PSOs with good optimization performance at present and simplified PSO proposed by this paper all are used to resolve the optimization problems of four widely used test functions, and the results show that simplified PSO has better optimization performance than others.

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Particle Swarm Optimization Neural Network and Its Application in Soft-Sensing Modeling

Cited by 20 publications

References 3 publications

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Simplified particle swarm optimization algorithm based on particles classification

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