### Abstract

Associative memories are capable of memorizing particular patterns and recalling them from their partial information. Different from simple associative memory models based on Hopfield neural networks with sigmoid neurons, a particular model based on the chaotic neural network was also proposed for dynamic associative memory, which can generate various patterns from given information. However, the chaotic network model is so complicated that its behavior has not been analyzed well and can't be controlled easily. To the contrary, this paper shows that a discrete-time simple associative memory model with Euler's difference scheme has possibility to generate chaos. It follows that even such a simple model can be used for dynamic associative memory. Numerical examples also confirm the emergence of chaotic trajectories of the model and demonstrate their use for dynamic associative memory.

Original language | English |
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Title of host publication | Proceedings of the SICE Annual Conference |

Pages | 1444-1450 |

Number of pages | 7 |

Publication status | Published - 2010 |

Event | SICE Annual Conference 2010, SICE 2010 - Taipei, Taiwan, Province of China Duration: 2010 Aug 18 → 2010 Aug 21 |

### Other

Other | SICE Annual Conference 2010, SICE 2010 |
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Country | Taiwan, Province of China |

City | Taipei |

Period | 10/8/18 → 10/8/21 |

### Fingerprint

### Keywords

- Chaotic dynamical system
- Dynamic associative memory
- Hopfield neural network
- Nonlinear optimization
- Stability analysis

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications

### Cite this

*Proceedings of the SICE Annual Conference*(pp. 1444-1450). [5602010]

**Dynamic associative memory by using chaos of a simple associative memory model with Euler's finite difference scheme.** / Masuda, Kazuaki; Aiyoshi, Eitaro.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the SICE Annual Conference.*, 5602010, pp. 1444-1450, SICE Annual Conference 2010, SICE 2010, Taipei, Taiwan, Province of China, 10/8/18.

}

TY - GEN

T1 - Dynamic associative memory by using chaos of a simple associative memory model with Euler's finite difference scheme

AU - Masuda, Kazuaki

AU - Aiyoshi, Eitaro

PY - 2010

Y1 - 2010

N2 - Associative memories are capable of memorizing particular patterns and recalling them from their partial information. Different from simple associative memory models based on Hopfield neural networks with sigmoid neurons, a particular model based on the chaotic neural network was also proposed for dynamic associative memory, which can generate various patterns from given information. However, the chaotic network model is so complicated that its behavior has not been analyzed well and can't be controlled easily. To the contrary, this paper shows that a discrete-time simple associative memory model with Euler's difference scheme has possibility to generate chaos. It follows that even such a simple model can be used for dynamic associative memory. Numerical examples also confirm the emergence of chaotic trajectories of the model and demonstrate their use for dynamic associative memory.

AB - Associative memories are capable of memorizing particular patterns and recalling them from their partial information. Different from simple associative memory models based on Hopfield neural networks with sigmoid neurons, a particular model based on the chaotic neural network was also proposed for dynamic associative memory, which can generate various patterns from given information. However, the chaotic network model is so complicated that its behavior has not been analyzed well and can't be controlled easily. To the contrary, this paper shows that a discrete-time simple associative memory model with Euler's difference scheme has possibility to generate chaos. It follows that even such a simple model can be used for dynamic associative memory. Numerical examples also confirm the emergence of chaotic trajectories of the model and demonstrate their use for dynamic associative memory.

KW - Chaotic dynamical system

KW - Dynamic associative memory

KW - Hopfield neural network

KW - Nonlinear optimization

KW - Stability analysis

UR - http://www.scopus.com/inward/record.url?scp=78649271900&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649271900&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78649271900

SN - 9784907764364

SP - 1444

EP - 1450

BT - Proceedings of the SICE Annual Conference

ER -