Particle swarm algorithm based optimization method for microbial fermentation

A technology of microbial fermentation and particle swarm algorithm, applied in the field of microbial fermentation, can solve the problem of difficult to obtain the optimal fermentation individual and optimal fermentation control parameters, and achieve the effect of improving the overall quality and ensuring the excellent quality.

Inactive Publication Date: 2015-12-02
PUTIAN UNIV
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Problems solved by technology

[0007] In order to solve the above-mentioned technical problems, the present invention provides a microbial fermentation optimization method based on particle swarm optimization algorithm, which is used to solve the n

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  • Particle swarm algorithm based optimization method for microbial fermentation

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[0027] In order to describe the technical content, structural features, achieved goals and effects of the present invention in detail, the following will be described in detail in conjunction with the embodiments and accompanying drawings.

[0028] The embodiment of the present invention is a microbial fermentation optimization method based on the particle swarm optimization algorithm. The particle swarm optimization algorithm (ParticleSwarmOptimization, PSO) is an effective global optimization algorithm, which was first proposed by Kennedy and Eberhart in the United States in 1995. The flock of birds foraging process was later inspired by this model, and the particle swarm optimization algorithm was used to solve the optimization problem. In the particle swarm algorithm, the solution of each optimization problem is regarded as a bird in the search space, that is, a "particle". Firstly, the initial population is generated, that is, a group of particles is randomly initialized ...

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Abstract

The present invention discloses a particle swarm algorithm based optimization method for microbial fermentation. The method comprises the following steps of: establishing a microbial fermentation data set; randomly dividing the microbial fermentation data set into two parts: a training data set and a test data set; building up a BP neural network; training the BP neural network; executing a binary encoding of a control parameter of the microbial fermentation to obtain an initial particle swarm; calculating a fitness value of each particle by using the BP neural network as a fitness function; executing a self-extremum operator of a particle; executing a global extremum operator of the particle; executing a speed-displacement model operating operator; and calculating the fitness value of a new-generation particle after changing location by using the BP neural network as the fitness function, to obtain an optimal control parameter combination. The method disclosed by the invention can obtain the optimal control parameter combination according to the existing fermentation data without redesigning an experiment.

Description

technical field [0001] The invention relates to the field of microbial fermentation, in particular to a method for optimizing microbial fermentation based on a particle swarm algorithm. Background technique [0002] The microbial fermentation process is a highly complex and nonlinear process, and it is difficult to model the fermentation process with an accurate mathematical model. In recent years, with the development of computational intelligence algorithms, more and more computational intelligence algorithms have been applied to the modeling and optimization control of microbial fermentation processes. For example, BP neural network, genetic algorithm, etc. are used to model and optimize the fermentation process. In the prior art, the BP neural network and the genetic algorithm are often applied alone to the modeling and optimization control of the fermentation process. There is also a joint application of BP neural network and genetic algorithm to model and optimize th...

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Application Information

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IPC IPC(8): G06F19/12G06N3/02
Inventor 彭建升
Owner PUTIAN UNIV
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