Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Application of spiking neural networks and the bees algorithm to control chart pattern recognition

Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Bio-Inspired Obstacle Avoidance: from Animals to Intelligent Agents

A considerable amount of research in the field of modern robotics deals with mobile agents and their autonomous operation in unstructured, dynamic, and unpredictable environments. Designing robust controllers that map sensory input to action in order to avoid obstacles remains a challenging task. Several biological concepts are amenable to autonomous navigation and reactive obstacle avoidance. We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem. We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics. We emphasize the advantages and highlight potential drawbacks of each approach. We also identify the benefits of using biological principles in artificial intelligence in various research areas

Application of spiking neural networks and the bees algorithm to control chart pattern recognition

Statistical process control (SPC) is a method for improving the quality of products. Control charting plays a most important role in SPC. SPC control charts arc used for monitoring and detecting unnatural process behaviour. Unnatural patterns in control charts indicate unnatural causes for variations. Control chart pattern recognition is therefore important in SPC. Past research shows that although certain types of charts, such as the CUSUM chart, might have powerful detection ability, they lack robustness and do not function automatically. In recent years, neural network techniques have been applied to automatic pattern recognition. Spiking Neural Networks (SNNs) belong to the third generation of artificial neural networks, with spiking neurons as processing elements. In SNNs, time is an important feature for information representation and processing. This thesis proposes the application of SNN techniques to control chart pattern recognition. It is designed to present an analysis of the existing learning algorithms of SNN for pattern recognition and to explain how and why spiking neurons have more computational power in comparison to the previous generation of neural networks. This thesis focuses on the architecture and the learning procedure of the network. Four new learning algorithms arc presented with their specific architecture: Spiking Learning Vector Quantisation (S-LVQ), Enhanced-Spiking Learning Vector Quantisation (NS-LVQ), S-LVQ with Bees and NS-LVQ with Bees. The latter two algorithms employ a new intelligent swarm-based optimisation called the Bees Algorithm to optimise the LVQ pattern recognition networks. Overall, the aim of the research is to develop a simple architecture for the proposed network as well as to develop a network that is efficient for application to control chart pattern recognition. Experiments show that the proposed architecture and the learning procedure give high pattern recognition accuracies

Efficient Learning Machines

Computer scienc

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments