Hardware Realization of Neural Oscillator with Learning Capability

在这项研究中，我提出了一种基于同时摄动优化方法的脉冲耦合振荡器的学习方案及其硬件实现。由于脉冲耦合振荡器是一种递归神经网络，通常的优化方法难以找到合适的参数值。同时摄动法给出了一个简单的解。此外，该方法适合于硬件实现。从这一角度出发，提出并制作了具有学习能力的硬件脉冲耦合振荡器和具有学习能力的递归神经网络，首先通过C语言和MatLab的仿真验证了所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期望间隔的脉冲序列，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的是S；数字实现和模拟实现。作为前一种方法，现场可编程门阵列(现场可编程门阵列)是有用的目标。用VHDL语言设计了具有学习功能的脉冲耦合振荡器。然后，将设计结果在现场可编程门阵列上进行配置。验证了具有学习能力的FPGA脉冲耦合振荡器系统的运行情况。获得了合适的脉冲序列。第二种方法是模拟实现。然后，采用现场可编程模拟阵列(FPAA)实现振荡器。对系统进行了模拟电路设计。电路运行由电路仿真器确认。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。脉冲序列产生的间隔随脉冲耦合振荡器中参数的不同而不同，并且利用同步微扰法在现场可编程门阵列上实现了一些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硬件递归神经网络系统很难实现。然而，我使用同时摄动法实现了具有学习能力的Hopfield神经网络系统和双向神经网络系统。给出了这些系统的一些应用，验证了同时摄动法设计的具有学习能力的脉冲耦合振荡器的有效性和可行性。制造了这些系统，并测试了这些系统的运行情况。较少

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