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Power Electronics Converters Applications And Design

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

In this paper, an attempt has been made to review the applications of artificial neural networks (ANN) for energy and exergy analysis of refrigeration, air conditioning and heat pump (RACHP) systems. The studies reported are categorized into eight groups as follows: (i) vapour compression systems (ii) RACHP systems components, (iii) vapour absorption systems, (iv) prediction of refrigerant properties (v) control of RACHP systems, (vi) phase change characteristics of refrigerants, (vii) heat ventilation air conditioning (HVAC) systems and (viii) other special purpose heating and cooling applications. More than 90 published articles in this area are reviewed. Additionally, the limitations with ANN models are highlighted. This paper concludes that ANN can be successfully applied in the field of RACHP systems with acceptable accuracy.

Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review

Proceedings of world academy of science, engineering and technology, vol 8

Multicompartment emulation is an essential step to enhance the biological realism of neuromorphic systems and to further understand the computational power of neurons. In this paper, we present a hardware efficient, scalable, and real-time computing strategy for the implementation of large-scale biologically meaningful neural networks with one million multi-compartment neurons (CMNs). The hardware platform uses four Altera Stratix III field-programmable gate arrays, and both the cellular and the network levels are considered, which provides an efficient implementation of a large-scale spiking neural network with biophysically plausible dynamics. At the cellular level, a cost-efficient multi-CMN model is presented, which can reproduce the detailed neuronal dynamics with representative neuronal morphology. A set of efficient neuromorphic techniques for single-CMN implementation are presented with all the hardware cost of memory and multiplier resources removed and with hardware performance of computational speed enhanced by 56.59% in comparison with the classical digital implementation method. At the network level, a scalable network-on-chip (NoC) architecture is proposed with a novel routing algorithm to enhance the NoC performance including throughput and computational latency, leading to higher computational efficiency and capability in comparison with state-of-the-art projects. The experimental results demonstrate that the proposed work can provide an efficient model and architecture for large-scale biologically meaningful networks, while the hardware synthesis results demonstrate low area utilization and high computational speed that supports the scalability of the approach.

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons

In this paper by using MultiLayer Perceptron and Radial Basis Function (RBF) neural networks, a novel method for solving both kinds of differential equation, ordinary and partial differential equation, is presented. From the differential equation and its boundary conditions, the energy function of the network is prepared which is used in the unsupervised training method to update the network parameters. This method was implemented to solve the nonlinear Schrodinger equation in hydrogen atom and triangle-shaped quantum well. Comparison of this method results with analytical solution and two well-known numerical methods, Runge-kutta and finite element, shows the efficiency of Neural Networks with high accuracy, fast convergence and low use of memory for solving the differential equations.

Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations

In this paper, the application of neural networks to study the design of short-term temperature forecasting (STTF) Systems for Kermanshah city, west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STTF systems is used. Our study based on MLP was trained and tested using ten years (1996-2006) meteorological data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STTF systems. Keywords—Artificial neural networks, Forecasting, Weather, Multi-layer perceptron.

Application of Artificial Neural Networks for Temperature Forecasting

— In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems. Keywords— Artificial neural networks, Forecasting, Multi-layer perceptron. I. I NTRODUCTION UMEROUS advances have been made in developing intelligent systems, some inspired by biological neural networks, fuzzy systems and combination of them. Researchers from many scientific disciplines are designing Artificial Neural Network (ANNs) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory and control. Conventional approaches have been proposed for solving these problems. Although successful applications can be found in certain well-constrained environments, none is flexible enough to perform well outside its domain. Artificial Neural Network has been replacing traditional methods in many applications offering, besides a better performance, a number of advantages: no need for system model, tolerance bizarre patterns, notable adaptive capability and so on. Load forecasting is one of the most successful applications of ANN in power systems. Neuro-Fuzzy (NF) computing is a popular framework for solving complicated(complex) problems. If we have knowledge expressed in linguistic rules, we can build a Fuzzy Interface System (FIS), and if we have data, or can learn from a simulation (training) then we can use ANNs. For building an FIS, we have to specify the fuzzy sets, fuzzy operators and the knowledge base. Similarly, for constructing an ANN for an application the user needs to specify the architecture and learning algorithm. An analysis reveals that the drawbacks pertaining to these approaches seem to be complementary and therefore it is

Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region

Modeling and implementation of biological neural networks are significant objectives of the neuromorphic research field In this field, neuronal synchronization plays a significant role in the processing of biological information This paper presents a set of piecewise linear (MLPWL1) and multiplierless piecewise linear (MLPWL2) neuron models, which mimic behaviors of different types of neurons, similar to the biological behavior of conductance-based neurons Both simulations and a low-cost digital implementation are carried out to compare the proposed models to a single ML neuron and two coupled ML neurons, demonstrating the required range of dynamics with a more efficient implementation Hardware implementations on a field-programmable gate array (FPGA) show that the modified models mimic the biological behavior of different types of neurons with higher performance and significantly lower implementation costs compared to the previous realizations of the ML model The mean normalized root mean square errors (NRMSEs) of the MLPWL1 and MLPWL2 models are 370% and 489%, respectively, as compared to the original ML model

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Digital Multiplierless Realization of Two Coupled Biological Morris-Lecar Neuron Model

This paper presents analytical expressions for the class-E power amplifier with MOSFET linear gate-to-drain and nonlinear drain-to-source parasitic capacitances at any duty ratio. The maximum operating frequency, output power capability, and element values as functions of the duty ratio are obtained. The element values are directly dependent upon the selection of duty ratio and require a careful duty ratio selection to minimize component power losses and to maximize the total efficiency. Two design examples at 25 and 9 W output power at 4-MHz operating frequency along with the PSpice-simulation and experimental waveforms are presented. It is shown from the derived expressions that the slope of the voltage across the MOSFET gate-to-drain parasitic capacitance during the switch-off state as a function of the duty ratio affects the switch-voltage waveform. Therefore, it is possible to achieve the required peak switch voltage and the class-E ZVS/ZVDS conditions simultaneously by adjusting the duty ratio. The theoretical results and PSpice simulations agreed with experimental results quantitatively, which shows the validity of the presented analysis.

Mohsen Hayati

Papers

Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations

Application of Artificial Neural Networks for Temperature Forecasting

Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region

Digital Multiplierless Realization of Two Coupled Biological Morris-Lecar Neuron Model

Analysis and Design of Class-E Power Amplifier With MOSFET Parasitic Linear and Nonlinear Capacitances at Any Duty Ratio