Chih-Min Lin

Integration of classifier diversity measures for feature selection-based classifier ensemble reduction

A classifier ensemble combines a set of individual classifier’s predictions to produce more accurate results than that of any single classifier system. However, one classifier ensemble with too many classifiers may consume a large amount of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a novel classifier ensemble reduction framework. The framework converts the ensemble reduction into an optimization problem and uses the harmony search algorithm to find the optimized classifier ensemble. Both pairwise and non-pairwise diversity measure algorithms are applied by the subset evaluation method. For the pairwise diversity measure, three conventional diversity algorithms and one new diversity measure method are used to calculate the diversity’s merits. For the non-pairwise diversity measure, three classical algorithms are used. The proposed subset evaluation methods are demonstrated by the experimental data. In comparison with other classifier ensemble methods, the method implemented by the measurement of the interrater agreement exhibits a high accuracy prediction rate against the current ensembles’ performance. In addition, the framework with the new diversity measure achieves relatively good performance with less computational time.

Enhanced Robotic Hand–Eye Coordination Inspired From Human-Like Behavioral Patterns

Robotic hand–eye coordination is recognized as an important skill to deal with complex real environments. Conventional robotic hand–eye coordination methods merely transfer stimulus signals from robotic visual space to hand actuator space. This paper introduces a reverse method. Build another channel that transfers stimulus signals from robotic hand space to visual space. Based on the reverse channel, a human-like behavior pattern: “Stop-to-Fixate,” is imparted to the robot, thereby giving the robot an enhanced reaching ability. A visual processing system inspired by the human retina structure is used to compress visual information so as to reduce the robot’s learning complexity. In addition, two constructive neural networks establish the two sensory delivery channels. The experimental results demonstrate that the robotic system gradually obtains a reaching ability. In particular, when the robotic hand touches an unseen object, the reverse channel successfully drives the visual system to notice the unseen object.

A novel approach to a mobile robot via multiple human body postures

This paper focuses on applying human postures and face tracking technologies to design an autonomous patrol vehicle control system, which contains a wireless video surveillance ability. The entire system includes the following several parts: (1) Obtaining the skeleton joints based on the Kinect skeleton tracking: the angles and distances between each human arms joint are calculated to be the input of an artificial neural networks. (2) Changing the vehicles gear box: the dynamic gesture recognition is built by using the artificial neural network and a finite state machine. (3) Controlling the vehicle speed, the speed is controlled by a fuzzy control algorithm. (4) Controlling of a motorized camera: the Kinect face tracking function is applied to detect a humans fact direction, so that, the motorized cameras rotation is controlled by the direction. This project expands the application range of intelligent mobile robots and improves the robotic autonomous ability to deal with complex environments.

Harmony Search Algorithm for Fuzzy Cerebellar Model Articulation Controller Networks Optimization

The general learning algorithm of Fuzzy Cerebellar Model Articulation Controller networks usually applies the gradient-descent type methods. However, these gradient-descent methods cause the high possibility to converging into local minima. To cope with the local minimum problem, we instead propose to apply harmony search algorithm to achieve better performances. The harmony search algorithm optimizes not only Fuzzy Cerebellar Model Articulation Controller network’s weight values, but also optimizes network receptive field’s centre positions and width parameters. To find the best optimized network, the weight values, centre positions, and width parameters are transformed to three data strings. In addition, an improved version of harmony search algorithm is used to search the best combination within data domains. The network’s performances are verified by approximating four non-linear formulae. The experimental results show that the improve harmony search algorithm performs very fast convergence speed.

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Abstract Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds.

Fuzzy cerebellar model articulation controller network optimization via self-adaptive global best harmony search algorithm

Fuzzy cerebellar model articulation controller (FCMAC) networks with excellent nonlinear appropriation ability and simple implementation are used to solve complex uncertainties problems in engineering applications. Both online and off-line learning algorithm of FCMAC networks usually applies the gradient-descent-type methods. However, such gradient-descent methods lead to the high possibility to converging into local minima. To cope with the local minimum problem, this paper alternatively proposes to apply harmony search algorithm to find optimal network parameters, so as to achieve better performances of FCMAC. The harmony search algorithm optimizes not only FCMAC network’s weight variables, but also optimizes network receptive field’s center position and standard deviation parameters. In order to obtain an optimal network, the weight values, center positions, and standard deviations are transformed to three data strings that can be processed by harmony search algorithm. In particular, the self-adaptive global best harmony search algorithm (SGHS) is used to search optimal parameter combinations of FCMAC within solution domains. The network’s performances are verified by approximating six nonlinear formulae. In order to compare the performances of the FCMAC networks optimized by the SGHS algorithm, a back-propagation trained network and another harmony search variant optimized networks are also tested in this work. The experimental results show that the networks optimized by SGHS perform the faster convergence speed and better accuracy.

Integration of fuzzy CMAC and BELC networks for uncertain nonlinear system control

This paper develops a fuzzy adaptive control system consisting of a new type of fuzzy neural network and a robust controller for uncertain nonlinear systems. The new designed neural network contains the key mechanisms of a typical fuzzy CMAC network and a brain emotional learning controller network. First, the input values of the new network are delivered to a receptive field structure that is inspired from the fuzzy CMAC. Then, the values are divided into a sensory and an emotional channels; and the two channels interact with each other to generate the final outputs of the proposed network. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; in addition, stability analysis theory is used to guaranty the proposed controllers convergence. In the experimentation, a “Duffing-Holmes” chaotic system and a simulated mobile robot are applied to verify the effectiveness and feasibility of the proposed control system. By comparing with the performances of other neural network based control systems, we believe our proposed network is capable of producing better control performances of complex uncertain nonlinear systems control.

A Developmental Learning Approach of Mobile Manipulator via Playing

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games.