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Processing method, system and equipment for visual cognition response data and medium

A technology that responds to data and processing methods, applied in the fields of computational intelligence and visual cognition, can solve problems such as low data accuracy, achieve enhanced search capabilities, improve search performance, strengthen search capabilities, and get rid of local optimal capabilities Effect

Pending Publication Date: 2022-08-05
KUNMING UNIV OF SCI & TECH
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

[0005] In the visual data processing of the prior art, no three suitable parameters in the ex-Gaussian model have been found in the parameter population to analyze the top-down and bottom-up cognitive processing and response processes in visual cognition, making Accuracy of data obtained is low

Method used

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  • Processing method, system and equipment for visual cognition response data and medium
  • Processing method, system and equipment for visual cognition response data and medium
  • Processing method, system and equipment for visual cognition response data and medium

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Embodiment 1

[0078] like figure 1 As shown, the method for processing visual cognitive response data based on the optimally selected flower algorithm provided by the embodiment of the present invention includes:

[0079] S101. Perform parameter initialization, and the algorithm parameters include the maximum number of iterations N_iter, the Levy flight index L, and the parameter [μ,σ,τ] population N.

[0080] S102: Calculate the fitness of each individual of the population according to the fitness function, obtain the global optimal individual of the current population, the maximum fitness value and the average fitness value of the population, and then calculate the transition probability p.

[0081] S103 , selecting j individuals with better fitness in the population to form an excellent individual sequence of the population.

[0082] S104. Randomly select 2 excellent individuals from the sequence of excellent individuals for crossover to obtain a new excellent individual, and compare t...

Embodiment 2

[0088] Based on the processing method for visual cognitive response data based on the flower algorithm based on optimal selection recorded in Embodiment 1, as a further embodiment,

[0089] In the described step S101, parameter initialization is performed, and the parameters include the maximum number of iterations N_iter=100, the Levi flight index L: Parameters [μ,σ,τ], population N=100.

[0090] In the step S102, the maximum fitness value and the average fitness value of the population are calculated, and then the specific steps for calculating the transition probability p are as follows:

[0091] Step2.1. Use the negative log-likelihood value of the initial parameter in the ex-Gaussian model of the reaction data as the objective function, and calculate the fitness value of each individual in the population:

[0092]

[0093] in is the probability density that obeys the ex-Gaussian distribution, i represents the i-th response time data obtained from a cognitive experi...

Embodiment 3

[0104] Based on the optimal selection-based flower algorithm processing method for visual cognitive response data described in Embodiment 1, as a further embodiment, in step S103, select j individuals with excellent fitness in various groups to form The specific steps of the excellent individual sequence of the population are as follows:

[0105] Dynamically select a certain number of excellent individuals j from the population composed of parameters to form a sequence of excellent individuals m={m 1 ,m 2 ,...m j }, where j

[0106]

[0107] where γ is a constant.

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Abstract

The invention belongs to the technical field of computing intelligence and visual cognition, and discloses a processing method, system and equipment for visual cognition response data and a medium. The method for processing the visual cognition reaction data through the flower algorithm based on optimal selection comprises the steps that a negative logarithm likelihood value of an ex-Gaussian model of reaction data in visual cognition serves as a target function; optimizing the target function by using an improved flower pollination-based algorithm to obtain parameters mu, sigma and tau corresponding to an optimal function fitness value; according to the method, the optimization ability of the algorithm is further enhanced, the optimization performance of the algorithm is improved, the method can be better applied to solving of the parameters of the ex-Gaussian model, and the method has the advantages of being high in robustness and high in robustness. Therefore, the corresponding relation between the three parameters and each stage in the visual cognition process can be more accurately detected.

Description

technical field [0001] The invention belongs to the technical field of computational intelligence and visual cognition, and in particular relates to a method, system, device and medium for processing visual cognitive response data. Background technique [0002] At present, with the continuous development of intelligent algorithms, they are almost used in various optimization fields. The flower algorithm is a typical algorithm among intelligent algorithms. This algorithm has the advantages of simple structure and few parameters, and has been applied by many scholars in the fields of medicine, electricity, and function optimization. However, this algorithm also has shortcomings such as easy to fall into local extremum and low convergence accuracy. Therefore, the present invention first optimizes the basic flower algorithm to improve the optimization performance of the algorithm. [0003] The human visual system continues to receive massive information input from the outside ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F17/18G06N3/00
CPCG06F30/27G06F17/18G06N3/006
Inventor 耿鑫钱谦冯勇伏云发殷继彬
Owner KUNMING UNIV OF SCI & TECH
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