In Situ parameter estimation of synchronous machines using genetic algorithm method

The paper presents an in situ parameter estimation method to determine the equivalent circuit parameters of the Synchronous Machines. The parameters of synchronous generator, both cylindrical rotor and salient pole rotor, are estimated based on the circuit model. Genetic algorithm based parameter estimation technique is adopted where only one set of in-situ measured load test data is used. Conventional methods viz., EMF, MMF, Potier triangle method uses rated voltage and rated current obtained from more than one operating condition to determine the parameters. However, Genetic Algorithm (GA) based method uses the working voltage and load current of a single operating point obtained from in-situ measured load test data to estimate the parameters. The test results of the GA based parameter estimation method are found to be closer to direct load test results and better than conventional methods

Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks

Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant interest in the development of efficient hardware implementations. In this work, we focus on designing energy efficient on-chip storage for the synaptic weights. In order to minimize the power consumption of typical digital CMOS implementations of such large-scale networks, the digital neurons could be operated reliably at scaled voltages by reducing the clock frequency. On the contrary, the on-chip synaptic storage designed using a conventional 6T SRAM is susceptible to bitcell failures at reduced voltages. However, the intrinsic error resiliency of NNs to small synaptic weight perturbations enables us to scale the operating voltage of the 6TSRAM. Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0.5%) in accuracy (22nm predictive technology). Scaling beyond that causes substantial performance degradation owing to increased probability of failures in the MSBs of the synaptic weights. We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due to decoupled read and write paths. In an effort to further minimize the area penalty, we present a synaptic-sensitivity driven hybrid memory architecture consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation framework shows that the proposed synaptic-sensitivity driven architecture provides a 30.91% reduction in the memory access power with a 10.41% area overhead, for less than 1% loss in the classification accuracy.Comment: Accepted in Design, Automation and Test in Europe 2016 conference (DATE-2016

Brain Inspired Enhanced Learning Mechanism Based on Spike Timing Dependent Plasticity (STDP) for Efficient Pattern Recognition in Spiking Neural Networks

Artificial neural networks, that try to mimic the brain, are a very active area of research today. Such networks can potentially solve difficult problems such as image recognition, video analytics, lot more energy efficiently than when implemented in standard von-Neumann computing machines. New algorithms for neural computing with high bio-fidelity are being developed today to solve hard machine learning problems. In this work, we used a spiking network model, and implemented a self-learning technique using a Spike Timing Dependent Plasticity (STDP) algorithm, that closely mimics the neural activity of the brain. The basic STDP algorithm modulates the synaptic weights interconnecting the neurons based on pairs of pre- and post-synaptic spikes. This ignores the timing information embedded in the frequency of the post-synaptic spikes. We calculated the average of the membrane potential of each column of neurons to give an idea of how it behaved and spiked for the particular output neuron for a particular image in the past .The update of the weights or the synapses are done on the basis of the frequency obtained. The resultant synaptic updates are less frequent and made wisely making the learning process better. With the present algorithm, we are able to achieve an accuracy of 79% for classifying images from the MNIST data set for a network of 400 output neurons. So the model was able to identify 79% of the total images correctly which is greater than the original STDP signifying that slow and sensible updates are definitely having a better impact on the learning process

Reward Modulated Spike Timing Dependent Plasticity Based Learning Mechanism in Spiking Neural Networks

Spiking Neural Networks (SNNs) are one of the recent advances in machine learning that aim to further emulate the computations performed in the human brain. The efficiency of such networks stems from the fact that information is encoded as spikes, which is a paradigm shift from the computing model of the traditional neural networks. Spike Timing Dependent Plasticity (STDP), wherein the synaptic weights interconnecting the neurons are modulated based on a pair of pre- and post-synaptic spikes is widely used to achieve synaptic learning. The learning mechanism is extremely sensitive to the parameters governing the neuron dynamics, the extent of lateral inhibition among the neurons, and the spike frequency adaptation parameters. Hence, we explore a reward modulated learning methodology to further improve the synaptic learning efficiency. In our work, we define a target spiking pattern a priori for each neuron in the network. The primary objective is to cause the actual neuronal spiking pattern to converge to the desired pattern during the training phase. The STDP driven synaptic updates are modulated by a reward metric, which determines the distance between the actual and target spike train. We estimated the reward using the difference between the averaged version of the actual and desired spike train. Reward based semi-supervised learning scheme is implemented on a two layered SNN trained to classify handwritten digits from the MNIST image set. We obtained an accuracy of 73.16% on the testing image set for 100 spiking neurons which helped learning better in case of supervision

Preparation and thermal decomposition of some oxomolybdenum(VI) oxalates

Anionic oxomolybdenum(VI) oxalates having the general formula A2[MoO3(C2O4] · H2O where A = K+ or NH4+ are prepared, characterized by chemical and infrared spectra and their thermal decomposition studied by DTA and TGA. A chain structure containing MoO6 octahedra linked through oxygen is proposed for the complex oxalates on the basis of i.r. absorption spectra. The ammonium compound decomposes endothermally around 270°C to give lower oxides of molybdenum which are finally oxidised to MoO3, while the final decomposition product of the potassium compound is K2MoO4