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Existence, uniqueness and global exponential stability of a periodic solution for a class of multidirectional associative memory neural network models

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Abstract

A multidirectional associative memory (MAM) neural network with periodic coefficients and distributed delays is studied. By constructing a Poincaré mapping, some sufficient conditions are obtained ensuring existence, uniqueness and the global exponential stability of a periodic solution of MAM neural network. The result is new to MAM neural networks. An example is given to illustrate the effectiveness of the result.

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Acknowledgments

This work was supported by the Natural Science Foundation of Hunan Province (Grant No. 06JJ2100) and the Science Foundation of Hunan Agricultural University (Grant No. 07WD08). The authors are grateful for the constructive comments of the referees.

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Correspondence to Tiejun Zhou.

Appendix

Appendix

1.1 Proof of Lemma 1

Proof

Suppose \(x(t) = (x_{11}(t), \dots , x_{1n_1}(t), \dots , x_{m1}(t), \dots , x_{mn_m}(t))^T\) is a \(T\)-periodic solution of MAM (2). Then,

$$\begin{aligned} {\int \limits _0^{ + \infty } {g_{pjki} (s)x_{pj} (t - s)\hbox{d}s} }&= {\sum \limits _{l = 0}^{ + \infty } {\int \limits _{lT}^{(l + 1)T} {g_{pjki} (s)x_{pj} (t - s)\hbox{d}s} } }\\ {}&= {\sum \limits _{l = 0}^{ + \infty } {\int \limits _0^T {g_{pjki} (u + lT)x_{pj} (t - u - lT)\hbox{d}u}.}} \end{aligned}$$

From assumption (H2) and the periodicity of \(x(t)\), we obtain

$$\begin{aligned} {\int \limits _0^{ + \infty } {g_{pjki} (s)x_{pj} (t - s)\hbox{d}s} }&= {\int \limits _0^T {\sum \limits _{l = 0}^{ + \infty } {g_{pjki} (u + lT)x_{pj} (t - u)} \hbox{d}u} } \\ {}&= {\int \limits _0^T {G_{pjki} (s)x_{pj} (t - s)\hbox{d}s}.} \end{aligned}$$

Therefore,

$$\begin{aligned} {\mathop{\mathop{\sum}\limits _{p = 1}}\limits_{p \ne k}^m} {\sum \limits _{j = 1}^{n_p } {w_{pjki} (t)f_{pj}\left( {\int \limits _0^{ + \infty } {g_{pjki} (s)x_{pj} \left( {t -s} \right) \hbox{d}s} } \right) } } = {\mathop{\mathop{\sum}\limits_{p = 1}}\limits_{p \ne k}^m} {\sum \limits_{j = 1}^{n_p } {w_{pjki} (t)f_{pj} \left( {\int \limits _0^T{G_{pjki} (s)x_{pj} ( {t - s} )\hbox{d}s} } \right) } }.\end{aligned}$$

It implies that \(x(t)\) is a \(T\)-periodic solution of MAM (4). Now, if \(x(t)\) is a \(T\)-periodic solution of MAM (4), then one can reverse the above sequence of steps and show that \(x(t)\) is also a \(T\)-periodic solution of MAM (2).

1.2 Proof of Lemma 2

Proof

From assumption (H2), we have

$$\begin{aligned} {\int \limits _0^T {G_{pjki} (s)\hbox{d}s} }&= {\int \limits _0^T {\sum \limits _{l = 0}^{ + \infty } {g_{pjki} (s + lT)} \hbox{d}s} = \sum \limits _{l = 0}^{ + \infty } {\int \limits _{lT}^{(l + 1)T} {g_{pjki} (u)\hbox{d}u} } } \\ {}&= {\int \limits _0^{ + \infty } {g_{pjki} (s)\hbox{d}s} = 1,} \end{aligned}$$

and

$$\begin{aligned} {\int \limits _0^T {\exp (\epsilon s) G_{pjki} (s) \hbox{d}s} }&= {\int \limits _0^T {\exp (\epsilon s) \sum \limits _{l = 0}^{ + \infty } {g_{pjki} (s + lT)} \hbox{d}s} = \sum \limits _{l = 0}^{ + \infty } {\int \limits _{lT}^{(l + 1)T} {\exp (\epsilon (u - lT)) g_{pjki} (u) \hbox{d}u} } } \\ {}&< {\sum \limits _{l = 0}^{ + \infty } {\int \limits _{lT}^{(l + 1)T} {\exp (\epsilon u) g_{pjki} (u) \hbox{d}u} } = \int \limits _0^{ + \infty } {\exp (\epsilon u) g_{pjki} (u)\hbox{d}u} } \\ {}&\le {\int \limits _0^{ + \infty } {\exp (u) g_{pjki} (u)\hbox{d}u} < +\infty .} \end{aligned}$$

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Wang, Y., Wang, M. & Zhou, T. Existence, uniqueness and global exponential stability of a periodic solution for a class of multidirectional associative memory neural network models. Neural Comput & Applic 26, 979–986 (2015). https://doi.org/10.1007/s00521-014-1772-0

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