Blind detection method of noise chaotic neural network based on discrete multilevel hysteresis

Abstract

The invention discloses a blind detection method of a noise chaotic neural network based on discrete multilevel hysteresis. The method includes the following steps: constructing a received data matrixXN; performing singular value decomposition on the received data matrix XN; setting a weight matrix WRI, and constructing a performance function; introducing a piecewise annealing function into the chaotic neural network to construct a discrete multilevel hysteretic chaotic neural network based on piecewise annealing; constructing a dynamic equation of an improved new model of the noise chaotic neural network based on discrete multilevel hysteresis, performing iterative operation on the dynamic equation of the improved new model, then substituting the result of each iteration into an energy function E(t) of the noise chaotic neural network based on discrete multilevel hysteresis, and when the energy function E(t) reaches a minimum value, determining that the chaotic neural network based on discrete multilevel hysteresis reaches a balance and the iteration ends. According to the scheme of the invention, an activation function is improved, a noise chaotic neural network model based on discrete multilevel hysteresis is constructed, and the phenomenon that the neural network falls into a minimal value point can be better avoided.

Description

technical field [0001] The invention relates to a blind detection method based on a discrete multi-level hysteresis noise chaotic neural network, which belongs to the field of wireless communication signal processing, and more specifically belongs to the technical field of Hopfield neural network blind detection. Background technique [0002] With the widespread application of discrete Hopfield neural networks in image restoration, associative memory, etc., the stability of the network is the basis of these applications, and the network must be stable to a fixed point at the end. Literature [Gao H.S., Zhang J., Stability for Discrete Hopfield Neural Networks with Delay [C]. 2008 Fourth International Conference on Natural Computation, Jinan, China, October 18-20, 2008, 560-563.] did some research on the stability of discrete neural networks, but these research results only Limited to image processing of binary or two-level signals. Literature[H.J.Liu,Y.Sun.Blind bilevel imag...

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Problems solved by technology

Literature [Zhang Yun, complex Hopfield neural network blind signal detection [D]. Nanjing: Nanjing University of Posts and Telecommunications Library, 2012:102-147.], and literature [Ruan Xiukai, Zhang Zhiyong. Blind detection of QAM signals based on continuous Hopfield neural network [J]. Journal of Electronics and Information Technology, 2011 (July 2011): 1-6.] Proposed discrete multi-level complex number and continuous multi-level complex Hopfield neural network respectively, but both are easy to fall into local minimum points, so The length of required data is large

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