Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals.     

Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of Robotic Manipulators

In this paper, hierarchical control techniques is used for controlling a robotic manipulator. The proposed method is based on the establishment of a non-linear mapping between Cartesian and joint coordinates using fuzzy logic in order to direct each individual joint. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control consists of solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Microbot with three degrees of freedom is utilized to evaluate this methodology. A decentralized fuzzy controller is used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint generates the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller is compared to a conventional controller. The simulation experiments indeed demonstate the effectiveness of the proposed method.     

An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices

The close price prediction model of the Zagreb Stock Exchange Crobex® index is presented in this paper. For the input/output data plan modeling the Crobex® index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5 days in advance it predicts the Crobex® close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex® index close price from 4 November 2010 to 24 January 2012 and the Crobex® index close price is predicted for the subsequent 5 days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5 days in advance and the Crobex® index close price is predicted in advance for the next 5 days starting with the last day of the input/output data set. In that way the Crobex® index close prices are predicted until 19 October 2012 based on the Crobex® index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex® index close price showing that the approach is useful for predicting within its limits.     

Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures

Biogeography Based Optimization (BBO) algorithm is one of the nature-inspired optimization methods, based on the study of geographical distribution of species on earth. The present research work is based upon decomposition of human electroencephalograms (EEG) signal by Wavelet Packet Transform, computation of a complete feature set using statistical and non-statistical properties followed by optimal selection of features by BBO; the optimality criterion being classification rate. The stopping criterion for BBO is set to 100% correct classification rate. The proposed algorithm is novel in terms of TWSVM computing the Habitat Suitability Index (HSI) values for BBO, perfect classification rate, low computation time, and feature selection mechanism. The proposed scheme outperforms several previous results reported in literature in that it gives a feature subset which gives 100% classification accuracy for different classification instances.