Abstract
Neural networks have many advantages, such as parallel processing, self-suit, associated memory and classify ability strongly which can be used to analog circuit fault diagnosis. But it is very easy to trap the local minimum if the initial network weights are randomly generated. To solve this problem, the cuckoo search algorithm is used to optimize the initial weights of the neural network. A novel method for analog circuit fault diagnosis is proposed in this paper, based on BP neural network as classifier optimized by cuckoo search algorithm. The feasibility and effectiveness of the proposed method are verified by the simulations of Sallen-Key low-pass filter circuit. Compared with other methods, the results show that the proposed method is effective to identify and classify faults.
Highlights
Since the 1960s, analog circuit fault diagnosis has made a lot of achievements, scholars have put forward many methods, such as Fault Dictionary, Statistical Identification, Gray Recognition method [1]
BP Neural network [5] has strong nonlinear mapping ability, dealing with complex nonlinear problems well, it become the first to use for analog circuit fault diagnosis and has been widely used
The faults diagnostic analysis for analog circuit faults based on firefly algorithm and extreme learning machine was proposed in ref. [12], the proposed method takes advantages of excellent classification capacity of extreme learning machine
Summary
Since the 1960s, analog circuit fault diagnosis has made a lot of achievements, scholars have put forward many methods, such as Fault Dictionary, Statistical Identification, Gray Recognition method [1]. The traditional dictionary diagnosis is the most effective method for the fault diagnosis of analog circuit currently, but it needs much works before the simulation and if the inputs don’t match the reserved values accurately, the system will fail to diagnosis [2]. The fundamental thought is converting the network using some representation sample to a general dictionary, and the diagnostic information is included in the network’ weigh [6] In this way, if you input the scale characteristic of the circuit, the diagnosis will be found out. The neural network will be optimized by cuckoo search algorithm to get improved classification performance for fault diagnosis. The rest of this paper is organized as follows: Section II introduces the algorithms of cuckoo search and the BP neural network structure in fault diagnosis.
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