| Summary: | 碩士 === 元智大學 === 電機工程學系 === 105 === ABSTRACT
It had the very big breakthrough in the machine learning development in recent years. No matter the Watson which developed by IBM or Google’s AlphaGo, they both are based on depth of neural networks. And the cerebellar model articulation controller (CMAC) has been widely used in various applications of neural networks, such as: inverted pendulum, nonlinear channel equalization and robot control. It has great generalization and learning fast characteristics enough to deal with the basic applications of neural network. But if the complexity of the higher non-linear task, there will be learning learning-difficulty situation. And the CMAC was originally designed for simple control applications, so high-dimensional input processing, such as: speech recognition which can’t be used by normal CMAC, so it need to improved.
This paper proposed the deep cerebellar model articulation controller(DCMAC) for echo cancellation and the MIMO-DCMAC with the Softmax function for speech recognition. We stack the conventional single-layered CMAC models into multiple layers to form a DCMAC model, and re-modify the back propagation algorithm to get the update of DCMAC’s parameter. Due to the deep structure, DCMAC can have a better generalization error than the normal CMAC. The experimental results also show that DCMAC can build model more effectively than CMAC in signal processing.
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