This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number of individual NNs employing a constructive strategy, the number of hidden nodes of individual NNs employing a constructive-pruning strategy, and different training samples for individual NN's learning. For diversity, negative correlation learning has been introduced and also variation of training samples has been made for individual NNs that provide better learning from the whole training samples. The major benefits of the proposed DEL compared to existing ensemble algorithms are (1) automatic design of ensemble; (2) maintaining accuracy and diversity of NNs at the same time; and (3) minimum number of parameters to be defined by user. DEL algorithm is applied to a set of real-world classification problems such as the cancer, diabetes, heart disease, thyroid, credit card, glass, gene, horse, letter recognition, mushroom, and soybean datasets. It has been confirmed by experimental results that DEL produces dynamic NN ensembles of appropriate architecture and diversity that demonstrate good generalization ability.

This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy logic. It is observed that NIC and CI intersect. The authors advocate and foresee more cross-fertilisation of the two emerging fields.

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands.Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep).Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations.Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.

Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Traveling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behavior of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

Operations on Fuzzy Sets 2.7 Linguistic Variables 2.7. / Features of Linguistic Variables 2.8 Linguistic Hedges 2.9 Fuzzy Relations 2.9.1 Compositional Rule of Inference 2.10 Fuzzy If-Then Rules 2.10.1 Rule Forms 2.10.2 Compound Rules 2.10.3 Aggregation of Rules 2.11 Fuzzification 2.12 Defuzzification 2.13 Inference Mechanism 2.13.1 Mamdani Fuzzy Inference 2.13.2 Sugeno Fuzzy Inference 2.13.3 Tsukamoto Fuzzy Inference 2.14 Worked Examples 2.15 MATLAB® Programs References 3 Fuzzy Systems and Applications 3.1 References 100 4 Neural Networks 103 4.1 Introduction 4.2 Artificial Neuron Model 106 4.3