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Free Online Library Improved GSO optimized ESN soft sensor model of flotation process based on multisource heterogeneous information fusion Research Article echo state network glowworm swarm optimization Report by The Scientific World Journal Biological sciences Environmental issues Mathematical optimization Usage Optimization theory Principal components analysis Sensors Models
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Optimize more memory for a large function With O the compiler tries to reduce code size and execution time without performing any optimizations that take a great deal of compilation time Oturns on the following optimization flags fauto inc dec fbranch count reg fcombine stack adjustments fcompare elim fcprop registers fdce
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Improved GSO Optimized ESN Soft Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an echo state network ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization GSO algorithm is proposed
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Then important parameters [Formula see text] [Formula see text] [Formula see text] of MLSSVM model are optimized by using the Improved Cuckoo Search ICS optimization algorithm In the end the hybrid ICS MLSSVM soft sensor model is developed by using optimized model parameter values and the key biochemical variables of the L lysine fermentation process are realized online
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Improved GSO Optimized ESN Soft Sensor Model of Flotation Flotation process is used to separate useful minerals and gangue based on the differences of the surface property of solid materials Figure 1 is a ty¶meters indies that a flotation process is calculated as
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2024 Wang et al The Scientific World Journal For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an echo state network ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization GSO algorithm is
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The molecules of a collector are generally composed of two parts 1 a hydrocarbon nonpolar and carboxyl group and 2 an amino acid group and another polar chemically active group affixed to the mineral surface Collectors are classified as nonionogenic and ionogenic according to their ability to dissociate into ions in an aqueous medium
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Finally the ESN networkstructureparametersareoptimizedbytheimproved GSO algorithm to realize the accurate prediction of the concentrate grade and tailings recovery rate in the otation process Considering a multiinput multioutput MIMO system the training sample set can be expressed as ={ =1 } is the output variable
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Improved GSO Optimized ESN Soft Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion Jie sheng Wang Shuang Han Na na Shen Affiliations Jie sheng Wang School of Electronic and Information Engineering University of Science Technology Liaoning Anshan 114044 China
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The ESN soft sensor model of flotation process is optimized by the GSO algorithm with congestion factor Simulation results show that the model has better generalization and prediction accuracy to meet the online soft sensor requirements of the real time control in the flotation process 1 Introduction
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Improved GSO Optimized ESN Soft Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion The Scientific World Journal Egypt doi /2024/262368 Full Text Open PDF Abstract Available in full text Categories Biochemistry Medicine Genetics Molecular Biology Environmental Science
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The ESN soft sensor model of flotation process is optimized by the GSO algorithm with congestion factor Simulation results show that the model has better generalization and
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Wang et al proposed an echo state network ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization GSO algorithm Simulation results show that the model has better generalization and prediction accuracy [ 16 ]
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For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an echo state network ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization GSO algorithm is proposed Firstly the color feature saturation and brightness and texture features angular second moment sum entropy inertia moment etc based
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Improved GSO Optimized ESN Soft Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion The Scientific World Journal Biochemistry Medicine Genetics Molecular Biology Environmental Science 2024 English Biogeography Based Optimization Algorithm for Designing of Planar Steel Frames
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The ESN soft sensor model of flotation process is optimized by the GSO algorithm with congestion factor Simulation results show that the model has better generalization and prediction accuracy to meet the online soft sensor requirements of the real time control in the flotation process Free full text ScientificWorldJournal 2024 2024 262368
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The ESN soft sensor model of flotation process is optimized by the GSO algorithm with congestion factor Simulation results show that the model has better generalization and prediction accuracy to meet the online soft sensor requirements of the real time control in the flotation process
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Improved GSO Optimized ESN Soft Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an echo state network ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization GSO algorithm is proposed
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Flotation is the primary concentration process based upon the interfacial chemistry of mineral particles in solution other processes based on surface properties include For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an echo state network ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization
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Hybrid Multiple Soft Sensor Models of Grinding Granularity Based on Cuckoo Searching Algorithm and Hysteresis Switching Strategy Scientific Programming /2024/146410 2024 Vol 2024 pp 1 11 Cited By 4 Author s Jie Sheng Wang Na Na Shen Keyword s
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Improved GSO optimized ESN soft sensor model of flotation process based on multisource heterogeneous information fusion Authors Jie sheng Wang 1 Shuang Han 2 Na na Shen 2 Affiliations
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For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an ESN based fusion soft sensor model optimized by the improved glowworm swarm optimization GSO algorithm is proposed For predicting the key technology indicators concentrate grade and tailings recovery rate of flotation process an echo state network ESN based fusion soft
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Na Na Shen is an academic researcher from University of Science and Technology Liaoning The author has contributed to research in topic s Soft sensor Kernel principal component analysis The author has an hindex of 2 co authored 4 publication s receiving 26 citation s
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The advantage of the GSO is that it simultaneously captures multiple optima of multimodal functions However its disadvantage is that it has a low search accuracy and low efficiency in the latter iterative part Also the performance of GSO is poor in high dimensional problems
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In order to overcome above disadvantages of GSO this paper presented an improved GSO algorithm which called parallel hybrid mutation glowworm swarm optimization PHMGSO algorithm Experimental results show that PHMGSO has higher calculation accuracy and convergence faster speed compared to standard GSO and PSO algorithms Keywords GSO PSO
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