Fuzzy Recognition Theory Research Articles

Method of intelligent agricultural pest image recognition based on machine vision algorithm

In recent years, people have paid increasing attention to smart agriculture. Its core technology is to combine information technology such as computer vision with agricultural production. In agricultural production, the occurrence of pests and diseases can seriously affect agricultural production. Therefore, in agricultural production, the diagnosis and identification of agricultural pests and diseases is the key to improve crop yield. Traditional machine vision methods often rely on feature extraction and classifier design, lack of strong learning and generalization ability, and can not effectively control crop diseases and pests in a timely manner. In order to solve this problem, this paper designs an agricultural pest recognition system based on machine vision algorithm of fuzzy recognition theory, and applies it to agricultural pest recognition. Compared with the traditional machine vision method, this method has obvious advantages, and can effectively improve the monitoring and early warning ability of crop diseases and pests. The experimental results show that the highest recognition rate of the machine vision optimization algorithm based on fuzzy recognition theory is 98.06%, and the lowest recognition error rate is 5.83%. It can be seen that the machine vision recognition algorithm based on fuzzy recognition theory has good recognition effect and can be used in agricultural pest recognition system to help farmers carry out agricultural production and reduce economic losses caused by diseases and pests.

Malware traffic detection based on type II fuzzy recognition

In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit. The primary objective of malware is to disrupt the normal functioning of computers and networks, all the while surreptitiously gathering users’ private and sensitive information. The formidable concealment and latency capabilities of malware pose significant challenges to its detection. In light of the operational characteristics of malware, this paper conducts an initial analysis of prevailing malware detection schemes. Subsequently, it extracts fuzzy features based on the distinct characteristics of malware traffic. The approach then integrates traffic detection techniques with Type II fuzzy recognition theory to effectively monitor malware-related traffic. Finally, the paper classifies the identified malware instances according to fuzzy association rules. Experimental results showcase that the proposed method achieves a detection accuracy exceeding 90%, with a remarkably low false alarm rate of approximately 5%. This method adeptly addresses the challenges associated with malware detection, thereby making a meaningful contribution to enhancing our country’s cybersecurity.

Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Risk assessment model of expansive soil slope stability based on Fuzzy-AHP method and its engineering application

ABSTRACTTo systematically evaluate the risk grade of stability of expansive soil slope during construction, the recognition model is established by using the fuzzy recognition theory and the analytic hierarchy process (AHP). Based on the characteristics of shallow slide of 22 expansive soil road sections constructed in China, the condition of slope body, swell–shrink grade, hydraulic and meteorological characteristics, supporting and improvement measures and other factors such as the construction technology and management level, vegetation coverage, soil layer, and drying–wetting cycles are selected as the evaluation indices, and the corresponding criteria of risk assessment system of expansive soil slope stability are also established. The weights of evaluation indices are obtained by using AHP method, and the measure functions are established to assess single index measurement and synthetic measurement. The maximum membership degree criterion is adopted to discern the risk grade of expansive soil slope stability. Based on this, the stability of expansive soil slope with different condition located in Nanyou highway and Beijing west 6th ring highway of China are analysed to verify the reliability of the present method. For practical purposes, the present risk assessment methodology can be used to systematically assess the slope stability in expansive soil zones.

Grey fuzzy comprehensive evaluation of regional financial innovation ability based on two types weights

Purpose – According to the grey uncertainty and the connotation of different types weights, the purpose of this paper is to establish the pattern of multi-dimensional grey fuzzy decision making with feedback based on weight vector and weight matrix, and applies this pattern to evaluate the regional financial innovation ability. Design/methodology/approach – At first, this paper analyzes the connotation of financial innovation ability and establishes the evaluation system of regional financial innovation ability. Second, the formula of computing the multi-objective weighted comprehensive value based on weight vector and weight matrix is put forward. In view of the object function with supervised factor and stability coefficient, this paper gives the formulas to compute weight vector and weight matrix. Moreover, the algorithm of the multi-dimensional grey fuzzy decision making pattern with feedback based on weight vector and weight matrix is expressed. At last, this paper uses the presented pattern to evaluate the financial innovation ability of thirty-one provinces in China. Findings – The results are convincing: the development of regional financial innovation is not balanced in China, having obvious spatial clustering feature. The comparisons of evaluation results based on different forms of weights show that the calculating convergence speed of the pattern presented in this paper is fast. The pattern enhances the rationality of the demarcation point between categories, and the convergence within categories, making the evaluation more reasonable. Practical implications – The method exposed in the paper can be used at evaluating the regional financial innovation ability and even for other similar evaluation problem. Originality/value – The paper succeeds in realising both the pattern of multi-dimensional grey fuzzy decision making with feedback and evaluating the regional financial innovation ability by using the newest developed theories: weighted grey and fuzzy recognition theory based on weight vector and weight matrix.

Application of Fuzzy Query Approach to the Fault Diagnosis of an Antiaircraft Gun's Servo system

Based on the theory of fuzzy recognition and fault diagnosis, this method only depends on experience and statistical data to set up fuzzy query relationship between the outside phenomena (fault characters) and the fault sources (fault patterns). From this relationship we can obtain the most possible fault sources, so that we can reach the goal of quick diagnosis.