Abstract
Because of its excellent small sample learning abilities and simple network structure, support vector machine (SVM) is widely applied in various pattern recognition fields, e.g., face recognition, scene classification, fault diagnosis, etc. Due to the complexity and diversity of analog circuit faults, the diagnosis accuracy and stability of SVM classifier optimized by traditional particle swarm optimization (PSO) are unsatisfactory. Therefore, this paper proposes an improved hybrid particle swarm optimization (IH-PSO) algorithm to optimize SVM, which is applied in the fault diagnosis of analog circuits. Compared with the traditional PSO algorithm, the proposed IH-PSO mainly has three improvements, namely, the opposition-based learning population initialization, the nonlinear time-varying inertia weight, and the new position updating strategy with a spiral convergence mechanism. The performance of the proposed IH-PSO algorithm is verified by 12 commonly used benchmark functions and experimental results show that the proposed IH-PSO algorithm overcomes the deficiencies of the traditional PSO algorithm, such as slow convergence speed and trapping into local optimums. In addition, to further verify the performance of IH-PSO algorithm, the IH-PSO optimized SVM is applied to solve analog circuits fault diagnosis problems. Extensive experiments are carried out and results indicate that the proposed method has better performances both in diagnosis accuracy and stability compared with that of the traditional method.
Highlights
With the broad application of analog circuits in the fields of communication, military, aviation, medical care, etc. [1], individuals have put forward higher demands for their security and reliability
In order to balance the global exploration and local exploitation ability, nonlinear timevarying inertia weight, opposition-based learning, and spiral convergence mechanism are introduced to the original particle swarm optimization (PSO) algorithm
In the first group of experiments, the proposed improved hybrid particle swarm optimization (IH-PSO) is compared with other four well-known swarm intelligence (SI) algorithms (BBO, MothFlame Optimization (MFO), KH and PSO) using 12 benchmark functions which are widely applied for performance evaluation of SI algorithms
Summary
With the broad application of analog circuits in the fields of communication, military, aviation, medical care, etc. [1], individuals have put forward higher demands for their security and reliability. Due to the continuity of components parameters, the insufficiency of diagnostic information in actual test circuits, as well as the tolerance impact of analog components [7]–[9], parameter fault diagnosis is rather complicated It is rather troublesome for traditional methods like signal processing or analytical models to meet the needs. X. Yuan et al.: Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized SVM have achieved good diagnosis results [12]. In the second group of experiments, the SVM optimized by IH-PSO method is applied to fault diagnosis of analog circuit and contrast experiments are conducted between the proposed IH-PSO and the original PSO. Fault diagnosis experimental results show that the IH-PSO optimized SVM has obvious advantages in diagnosis accuracy and stability, which indicate that the proposed IH-PSO has a good balance between global exploration and local exploitation.
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