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Hardware Realization of Neural Oscillator with Learning Capability

具有学习能力的神经振荡器的硬件实现

基本信息

批准号：
16500142
负责人：
MAEDA Yutaka
金额：
$ 0.9万
依托单位：
Kansai University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-16500142/
关键词：
neural oscillator FPGA learning simultaneous perturbation hardware implementation FPAA 同時摂動

项目摘要

In this research, I propose a learning scheme for pulse coupled oscillators using the simultaneous perturbation optimization method and its hardware implementation. It was difficult and complicated for usual optimization method to find proper parameter values of the pulse coupled oscillator, since the oscillator is a kind of recurrent neural network. The simultaneous perturbation method gives a simple solution. Moreover, this approach is suitable for hardware realization. From this point of view, I proposed and fabricated the hardware pulse coupled oscillator and recurrent neural network with learning ability via the simultaneous perturbation method.First of all, I confirm feasibility of the proposed pulse coupled oscillator with learning capability through simulation by C language and MatLab. The pulse coupled oscillator can generate pulse train with desired interval through leaning process.Hardware realization of neural networks is an interesting issue. Mainly there are two approache … More s ; digital realization and analog one. As the former approach, field programmable gate array(FPGA) is useful target. I designed the pulse coupled oscillator with learning capability by VHDL. Then, design result is configured on FPGA. I verified the operation of the FPGA pulse coupled oscillator system with learning ability. Proper pulse train is obtained.The second approach is analog realization. Then, field programmable analog array(FPAA) is adopted to implement the oscillator. Analog circuit design of the system is carried out. The circuit operation is confirmed by a circuit simulator. The system could realize the pulse coupled oscillator with learning capability via the simultaneous perturbation method. Next, the design is configured to FPAA. The circuit realized the pulse coupled oscillator. Interval of generated pulse train varied depending on parameters contained in the pulse coupled oscillator.Moreover, some recurrent neural networks with learning capability were realized by FPGA using the simultaneous perturbation method. Hopfield network and bidirectional associative memory are typical examples of the recurrent networks. Usually, it was difficult to realize these hardware recurrent neural network systems with learning capability. However, I implemented the Hopfield neural network system and bidirectional neural network system with learning ability using the simultaneous perturbation method. I showed some application of these systems.As a result, I could confirm a validity and feasibility of the pulse coupled oscillator with learning capability via the simultaneous perturbation method. These systems were fabricated and tested the operation of these systems. Less

在本研究中，我提出了一个脉冲耦合振荡器的学习方案，使用同时扰动优化方法及其硬件实现。由于脉冲耦合振荡器是一种递归神经网络，通常的优化方法很难找到合适的参数值。同时摄动法给出了一个简单的解决方案。该方法适合于硬件实现。从这个角度出发，本文提出并实现了硬件脉冲耦合振荡器和具有学习能力的递归神经网络的同时微扰方法。首先，通过C语言和MatLab仿真验证了本文提出的具有学习能力的脉冲耦合振荡器的可行性。脉冲耦合振荡器可以通过学习过程产生具有所需间隔的脉冲序列，神经网络的硬件实现是一个有趣的问题。主要有两种方法 ...更多信息数字实现和模拟实现。现场可编程门阵列（FPGA）作为前一种方法是很有用的目标。用VHDL语言设计了具有学习能力的脉冲耦合振荡器。然后，将设计结果在FPGA上进行配置。验证了FPGA脉冲耦合振荡器系统的运行具有学习能力。第二种方法是模拟实现。然后采用现场可编程模拟阵列（FPAA）来实现振荡器。进行了系统的模拟电路设计。通过电路模拟器确认电路操作。该系统通过同时微扰法实现了具有学习能力的脉冲耦合振荡器。接下来，将设计配置为FPAA。该电路实现了脉冲耦合振荡器。脉冲耦合振荡器产生的脉冲串的间隔随振荡器参数的变化而变化，并采用同时扰动法在FPGA上实现了具有学习能力的递归神经网络。Hopfield网络和双向联想记忆是递归网络的典型例子。通常，这些具有学习能力的递归神经网络系统很难用硬件实现。然而，我实现了Hopfield神经网络系统和双向神经网络系统的学习能力，使用同时扰动方法。通过对这些系统的应用，验证了用同时微扰法设计具有学习能力的脉冲耦合振荡器的有效性和可行性。制造这些系统并测试这些系统的操作。少