665 research outputs found

    Glowworm swarm optimisation for training multi-layer perceptrons

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

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    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

    Computational Optimizations for Machine Learning

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    The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

    Learning enhancement of radial basis function network with particle swarm optimization

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    Back propagation (BP) algorithm is the most common technique in Artificial Neural Network (ANN) learning, and this includes Radial Basis Function Network. However, major disadvantages of BP are its convergence rate is relatively slow and always being trapped at the local minima. To overcome this problem, Particle Swarm Optimization (PSO) has been implemented to enhance ANN learning to increase the performance of network in terms of convergence rate and accuracy. In Back Propagation Radial Basis Function Network (BP-RBFN), there are many elements to be considered. These include the number of input nodes, hidden nodes, output nodes, learning rate, bias, minimum error and activation/transfer functions. These elements will affect the speed of RBF Network learning. In this study, Particle Swarm Optimization (PSO) is incorporated into RBF Network to enhance the learning performance of the network. Two algorithms have been developed on error optimization for Back Propagation of Radial Basis Function Network (BP-RBFN) and Particle Swarm Optimization of Radial Basis Function Network (PSO-RBFN) to seek and generate better network performance. The results show that PSO-RBFN give promising outputs with faster convergence rate and better classifications compared to BP-RBFN

    Machine Learning for Microcontroller-Class Hardware -- A Review

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    The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa
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