CMAC is one useful learning technique that was developed two decades ago but yet lacks adequate theoretical foundation. Most past studies focused on development of algorithms, improvement of the CMAC structure, and applications. Given a learning problem, very little about the CMAC learning behavior such as the convergence characteristics, effects of hash mapping, effects of memory size, the err...

Cerebellar Model Articulation Controller Neural Networks (CMAC NN) is one of the intelligent systems used for modeling, identification, classification, and controlling of nonlinear systems. In this paper, the mathematical model of CMAC is presented. CMAC is implemented using Simulink environment and its parameters are tuned to get the best CMAC control action. Three different learning algorithm...

This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventiona...

The Cerebellar Model Articulation Controller (CMAC) is an influential brain-inspired computing model in many relevant fields. Since its inception in the 1970s, the model has been intensively studied and many variants of the prototype, such as KCMAC, MCMAC, and LCMAC, have been proposed. This review article focus on how the CMAC model is gradually developed and refined to meet the demand of fast...

The cerebellar model articulation controller (CMAC) (Albus 1975) is applied for learning the inverse dynamics of a simulated two joint, planar arm. The actuators were antagonistic muscles, which acted as feedback controllers for each joint. We use this example to demonstrate some limitations of the control paradigm used in earlier applications of the CMAC (e.g., Miller et al. 1987, 1990): the C...

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC is integrated into ASE controller. ASEreinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training concept is utilized to establish the look-up table of CMAC and to produce discrete control outputs. In compute...

In this study, we integrate the techniques of the cerebellar model articulation controller with general basis function (CMAC-GBF) systems and the support vector regression (SVR) approach to be a more efficient scheme. The advantages of the CMAC-GBF systems include: fast learning speed, guarantee learning convergence, capability of derivative, etc. On the other hand, a SVR is a novel method for ...

This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking single-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters. Compared with conventional CMAC, the DCMAC can characterize nonlinear transfo...

Vision has extensively expanded the robots capabilities, making the robot control problem more complex. To track a target with a robot arm in a three-dimensional space involves to use precise commands. We propose to insert a hierarchical neurocontroller based on CMAC (Cerebellar Model Articulation Controller) networks in a visual servoing loop. This hierarchical structure splits the robot’s wor...

The caudal cingulate motor area (CMAc) and the supplementary motor area (SMA) play important roles in movement execution. The present study examined the neural mechanisms underlying these roles by investigating local field potentials (LFPs) from these areas while monkeys pressed buttons with either their left or right hand. During hand movement, power increases in the high-gamma (80-120 Hz) and...