Minghui Shi

Integrate classifier diversity evaluation to feature selection based classifier ensemble reduction

Classifier ensembles improve the performance of single classifier system. However, a classifier ensemble with too many classifiers may occupy a large number of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a classifier ensemble reduction framework. The approach is implemented by using three conventional diversity algorithms and one new developed diversity measure method to calculate the diversitys merits within the classifier ensemble reduction framework. The subset evaluation method is demonstrated by the experimental data: the method not only can meet the requirements of high accuracy rate and fewer size, but also its running time is greatly shortened. When the accuracy requirements are not very strict, but the the running time requirements is more stringent, the proposed method is a good choice.

A connectionist model for 2-dimensional modal logic

The importance of bridging the gap between the connectionist and symbolic paradigms has been widely recognized. In this paper, we present a new connectionist model called CML2 for 2-dimensional modal logic program. After proposing a fix-point semantics of the logic program, we put forward an algorithm to build the CML2, which encodes the background knowledge represented by a 2-dimensional modal logic program into a recurrent neural network. We also prove the correctness of the algorithm.

Robotic 3D Reaching through a Development-Driven Double Neural Network Architecture

Reaching ability is a kind of human sensory motor coordination. The objective of this work is to imitate the developmental progress of human infant to create a robotic system which can reach or capture objects. The work proposes to employ a double neural network architecture to implement control a robotic system to learn reaching within 3D experimental environment. A constraint releasing mechanism is applied to implement the development procedure for the robot system. In addition, the experimental results are described and discussed in this paper.

Integration of brain-like computational structure and infant behaviorial pattern for robotic hand-eye coordination

Robotic hand-eye coordination plays an important role in dealing with real time environment; and the learning procedure of this skill affects the fundamental framework of robotic cognition. This paper introduces a novel developmental approach to hand-eye coordination in an autonomous robotic system. Existing work employs neural network models to map visual perception to hand. In the approach, a computational structure and a cross-modal link mechanism are applied to simulate brain cortices; and a movement pattern inspired by infant behaviors is designed to help robot learn to build its hand-eye coordination. This work is supported by experimental evaluation, which shows that the learning algorithm provides a fast and incremental learning of behavioral competence.

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.

A Developmental Approach to Robotic 3D Hand-Eye Coordination

Inspired by infant development and brain research, this paper, combining the two research hotspot: developmental robotics and robotic hand-eye coordination, proposes to employ incremental process to implement robotic hand-eye coordination using double-neural networks under the experimental condition of two cameras and a robotic arm. This setup is to imitate the developmental progress of human infant to capture objects; and the networks are implemented by the constructive network which can simulate infant’s brain development. Furthermore, this paper delicately describes the details of the experiment plan and steps to show the combining course of the two hotspot.

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.

Electroencephalogram-based brain-computer interface for the Chinese spelling system: a survey

Electroencephalogram (EEG) based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain’s usual output pathways such as muscles. A popular application for EEGs is the EEG-based speller, which translates EEG signals into intentions to spell particular words, thus benefiting those suffering from severe disabilities, such as amyotrophic lateral sclerosis. Although the EEG-based English speller (EEGES) has been widely studied in recent years, few studies have focused on the EEG-based Chinese speller (EEGCS). The EEGCS is more difficult to develop than the EEGES, because the English alphabet contains only 26 letters. By contrast, Chinese contains more than 11 000 logographic characters. The goal of this paper is to survey the literature on EEGCS systems. First, the taxonomy of current EEGCS systems is discussed to get the gist of the paper. Then, a common framework unifying the current EEGCS and EEGES systems is proposed, in which the concept of EEG-based choice acts as a core component. In addition, a variety of current EEGCS systems are investigated and discussed to highlight the advances, current problems, and future directions for EEGCS.

Towards Low-Cost P300-Based BCI Using Emotiv Epoc Headset

P300-based brain-computer interface (BCI) has been widely studied over two decades. However, there are several factors that hamper P300-based BCI to be used in daily life. EEG acquisition devices are often too much expensive for an average customer. Although the Emotiv Epoc headset is a kind of low-cost device for recording brain signals and has been adopted to develop some BCI systems, due to the limited number of electrodes, the Emotiv Epoc headset cannot cover the regions of scalp that are convenient for detecting P300, so the effectiveness of the Emotiv Epoc headset used in the P300-based BCI has been doubted by many researchers. This paper aims to examine the performance of Emotiv Epoc headset used in the P300-based BCI system. Six participants participated in the experiment and two paradigms were compared. The results demonstrated that P300 could be effectively detected from the brain signals recorded by the Emotiv Epoc headset, showing the promising future to develop low-cost P300-based BCI systems.