简单记忆记忆神经网络的异质共存吸引子和大尺度幅度控制

Q. Lai, Liang Yang
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引用次数: 0

摘要

提出了一种具有自连接、双向连接和单记忆突触的简单环形记忆神经网络。与现有的MNN相比,本文提出的MNN最大的特点是能够产生异质共存的吸引子和大规模的幅度控制。在MNN中发现了多种异质共存吸引子,包括具有稳定点的混沌、具有极限环的混沌、具有稳定点的极限环。通过增加参数值,MNN的混沌变量可以相应增加,其对应的区域非常宽,从而实现依赖参数的大范围幅度控制。建立了电路实现平台,仿真结果验证了该方法的有效性和可靠性。
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Heterogeneous Coexisting Attractors and Large-Scale Amplitude Control in a Simple Memristive Neural Network
This paper proposes a simple ring memristive neural network (MNN) with self-connection, bidirectional connection and a single memristive synapse. Compared with some existing MNNs, the most distinctive feature of the proposed MNN is that it can generate heterogeneous coexisting attractors and large-scale amplitude control. Various kinds of heterogeneous coexisting attractors are numerically found in the MNN, including chaos with a stable point, chaos with a limit cycle, a limit cycle with a stable point. By increasing the parameter values, the chaotic variables of the MNN can be accordingly increased and their corresponding areas are extremely wide, yielding parameter-dependent large-scale amplitude control. A circuit implementation platform is established and the obtained results demonstrate its validity and reliability.
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