taha.ppt

Neuromorphic circuits are typically used to emulate cortical structures and to explore principles of computation of the brain. But they can also be used to implement convolutional and deep networks. Here we demonstrate a proof of concept, using our latest multi-core and on-line learning reconfigurable spiking neural network chips.

This document provides an overview of Mahdi Hosseini Moghaddam's background and work applying machine learning and cognitive computing for intrusion detection. It discusses his education in computer science and engineering and awards. It then outlines the goals of the presentation to discuss real-world applications of machine learning rather than scientific details. The document proceeds to discuss problems with current intrusion detection systems, introduce concepts in machine learning and cognitive computing, and describe Mahdi's methodology and architecture for a hardware-based machine learning system using a cognitive processor to enable fast intrusion detection.

Abstract This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated.

Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications. This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.

The document provides an overview of artificial neural networks and biological neural networks. It discusses the components and functions of the human nervous system including the central nervous system made up of the brain and spinal cord, as well as the peripheral nervous system. The four main parts of the brain - cerebrum, cerebellum, diencephalon, and brainstem - are described along with their roles in processing sensory information and controlling bodily functions. A brief history of artificial neural networks is also presented.

International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.

This document presents an FPGA implementation of an artificial neural network using a modular approach. Key points: - The implementation uses a multilayer perceptron topology trained with the backpropagation algorithm. It allows networks of any size to be synthesized quickly. - The design achieves peak performance of 5.46 million connection updates per second during training and 8.24 million predictions per second during computation. - It was tested on a breast cancer classification problem, achieving 96% accuracy. - The paper emphasizes important FPGA design principles that make neural network development modular and parameterized. This allows the system to solve various neural network problems efficiently.

Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.

Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.

An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.

The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present. There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped. Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights. Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs. Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series

This document introduces neural networks and neuro-DEVS. It defines artificial neural networks and provides examples of single neuron and multi-layer network structures. It describes different types of neural networks including perceptrons, multi-layer perceptrons, backpropagation networks, Hopfield networks, and Kohonen feature maps. It discusses areas where neural networks can be useful and their limitations. It outlines the advantages of using neural networks and describes three main applications. It provides an overview of the neuro-atomic model and its use in DEVS simulations, giving an example of a solar energetic system model that uses a neural network as a sub-component.