TL;DR: It is demonstrated that the aa model is relevant for feature extraction and dimensionality reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, chemistry, text mining and collaborative filtering leading to highly interpretable representations of the dynamics in the data.

TL;DR: A finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems and three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics.

TL;DR: It can be found that the fractional-order four-cell cellular neural network proposed and investigated by means of numerical simulations does exhibit hyperchaotic phenomena over a wide range of values of some specified parameter.

TL;DR: This paper develops an algorithm capable of determining the step-changes in signals that occur whenever a device is turned on or off, and which allows for the definition of a unique signature (ID) for each device.

TL;DR: An mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs is proposed, in which a simple BP neural network is built for the model estimation.

TL;DR: The experimental results show that FOS-ELM has higher accuracy with fewer training time, better stability and short-term predictability than EOS- ELM.

TL;DR: This paper proposes a framework for approximate NMF which constrains the ℓ0-norm of the basis matrix, or the coefficient matrix, respectively, and demonstrates the benefits of these methods, which compare to, or outperform existing approaches.

TL;DR: The proposed Hybrid Artificial Bee Colony (HABC) algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms and is a competitive approach for data clustering.

TL;DR: The proposed algorithm makes full use of the affine invariant advantage of ASIFT and the efficient merit of SURF while avoids their drawbacks and demonstrates the robustness and efficiency of the proposed algorithm.

TL;DR: The global stability of the proposed neural network and the optimality of the neural solution are proven in theory and application orientated simulations demonstrate the effectiveness of this proposed method.

TL;DR: This work investigates the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search and showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

TL;DR: The learning ability of neural networks is used to design a robust adaptive backstepping controller that does not require the knowledge of the robot dynamics and gains are tuned on-line to minimize the velocity error and improve the trajectory tracking characteristics.

TL;DR: This paper uses textual information to aid the financial time series forecasting and presents a novel text mining approach via combining ARIMA and SVR (Support Vector Regression) to forecasting.

TL;DR: A sequential learning algorithm for a neural network classifier based on human meta-cognitive learning principles, which indicates the superior performance of McNN over reported results in the literature.

TL;DR: Experimental results show that, compared with NCA, FNCA not only significantly increases the training speed but also obtains higher classification accuracy, and comparative studies with the state-of-the-art approaches on various real-world datasets also verify the effectiveness of the proposed linear and nonlinear F NCA methods.

TL;DR: A novel modified binary differential evolution algorithm (NMBDE) inspired by the concept of Estimation of Distribution Algorithm and DE is proposed, which can efficiently maintain diversity of population and achieve a better tradeoff between the exploration and exploitation capabilities by cooperating with the selection operator.

TL;DR: Experimental results show that the WNN algorithm can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy datasets.

TL;DR: The dynamic analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov, and some new conditions concerning global exponential stability are obtained.

TL;DR: A subject transfer framework for EEG classification that can achieve positive knowledge transfer for improving the performance of EEG classification when the training set of the target subject is small owing to the need to reduce the calibration session is proposed.

TL;DR: A novel gender classification framework, which utilizes not only facial features, but also external information, i.e. hair and clothing, which improves classification accuracy, even when images contain occlusions, noise, and illumination changes.

TL;DR: Simulative and numerical results demonstrate the superior performance of the ZNN models for time-varying matrix inversion, as well as the efficacy of the G-M dynamic system (which has to be started with initial conditions sufficiently close to the desired initial inverse).

TL;DR: The proposed supervised sparse representation method for face recognition can achieve promising classification accuracy and is exploited as a heuristic strategy to achieve this goal.

TL;DR: The CPSFC algorithm utilizes CPSO to search the fuzzy clustering model, exploiting the searching capability of fuzzy c-means (FCM) and avoiding its major limitation of getting stuck at locally optimal values.

TL;DR: It is found that (undirected) graph built on the enlarged prior information is more meaningful, hence the boundaries of the clusters are more correct, and a novel model for generalizing the unsupervised spectral clustering to semi-supervised spectrum clustering is presented.

TL;DR: The results indicate that the most abundant FFL configurations are often either the least sensitive to system parameters variation (IFFLs) or the least noisy (CFFLs), which can well explain the why FFLs are network motifs and are selected by nature in evolution.

TL;DR: A novel high-performance classification system based on the S-transform and a probabilistic neural network (PNN) is proposed that reduces the time required for learning and the computational costs associated with the features and the PNN's memory space.

TL;DR: A hybrid ant colony optimization (HACO) is presented, which prevents the search process from getting trapped in the local optimal solution and improves the convergence speed by adjusting pheromone approach and introducing a disaster operator.

TL;DR: This paper forms this adaptation problem as learning under covariate shift, and proposes a computationally efficient probabilistic classification method based on adaptive importance sampling, which is demonstrated in real-world human activity recognition.

TL;DR: This paper addresses issues through a classification system using two optimization techniques, the genetic algorithms and simulated annealing, that detects the best discriminative features and estimates the best SVM kernel parameters in a fully automatic way.