2,837 research outputs found

    Solution of Linear Programming Problems using a Neural Network with Non-Linear Feedback

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    This paper presents a recurrent neural circuit for solving linear programming problems. The objective is to minimize a linear cost function subject to linear constraints. The proposed circuit employs non-linear feedback, in the form of unipolar comparators, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit compares favorably with other proposed circuits for the same task. PSPICE simulation results are presented for a chosen optimization problem and are found to agree with the algebraic solution. Hardware test results for a 2–variable problem further serve to strengthen the proposed theory

    Computational neural learning formalisms for manipulator inverse kinematics

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    An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints

    Statistical physics of neural systems with non-additive dendritic coupling

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    How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such non-additive dendritic processing on single neuron responses and the performance of associative memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality

    Grid-Connected Single-Star Bridge-Cells Modular Multilevel Cascaded Converter with Selective Harmonic Elimination Techniques

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    Nowadays, Renewable Energy Sources (RESs) are receiving enormous attention due to the noticeable exhaustion of fossil fuel and its emission of greenhouse gases. DC-AC converters have attracted the attention of the researchers, as they are entailed to integrate RESs to the grid to comply with the grid frequency and voltage requirements. Due to the high penetration of RESs, especially with elevated power levels, high-power converters are needed, which necessitates higher voltage and current ratings of the semiconductor devices. The unavailability of high voltage semiconductor devices has directed the attention to the use of either series connection of semiconductor devices or Multilevel Inverters (MLIs). MLIs allow using several low rated semiconductors to hold the high output power of the inverter. The MLI output waveform is close to sinusoidal in nature, therefore it may require a small filter to enhance the output power quality. There are many types of MLIs, where the most common MLIs are Flying Capacitor, Diode Clamped, and Modular Multilevel Cascaded Converter (MMCC). The MMCC can be classified into three main formations, the Single-Star Bridge-Cells MMCC (SSBC-MMCC), the Double-Star Bridge-Cells MMCC (DSBC-MMCC), and the Double-Star Chopper-Cells MMCC (DSCC-MMCC). The main advantage of the MMCC is the modularity and scalability. In addition, the MMCC does not require any clamping diodes or flying capacitors for clamping the voltage across the switches. In this thesis, the MMCC will be used to integrate high-power RESs to Grid. Nevertheless, the high-power applications necessitate low switching frequency operations. One of the most common controlling techniques of MLI with low frequency operation is the Selective Harmonic Elimination (SHE). SHE insures also the output current Total Harmonic Distortion (THD) to be minimized. One disadvantage of the SHE method is that the complexity of the algorithm along with the equations used is increased by the increase of the MMCC number of levels. Therefore, other alternatives of SHE techniques will be studied in this work to overcome this complexity. This thesis focuses typically on MMCC, particularly the SSBC-MMCC. In this work, a high-power grid-connected SSBC-MMCC is controlled with three different SHE techniques, complying with low switching frequency operation limitation in high-power applications. In addition to the Conventional SHE (C-SHE) technique, Quasi-SHE (Q-SHE) and Asymmetrical-SHE (A-SHE) approaches are proposed and assessed. Q-SHE and A-SHE approaches are based on eliminating selected low order harmonics (for instance, eliminating the fifth and seventh order harmonics), irrelevant to the number of employed levels provided that the number of levels allows for the required harmonic elimination. Compared with the C-SHE approach, the Q-SHE and A-SHE require less computational burden in solving the required equation groups, especially when a high number of levels and/or multiple switching angles for each voltage level are needed, while maintaining the same dv/dt of the output voltage. A 5MW, 17-level, grid-connected SSBC-MMCC, controlled in the synchronous rotating reference frame, is employed for assessing the addressed SHE techniques. The assessment is validated through simulation results using Matlab/Simulink platform

    Verification of Sigmoidal Artificial Neural Networks using iSAT

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    This paper presents an approach for verifying the behaviour of nonlinear Artificial Neural Networks (ANNs) found in cyber-physical safety-critical systems. We implement a dedicated interval constraint propagator for the sigmoid function into the SMT solver iSAT and compare this approach with a compositional approach encoding the sigmoid function by basic arithmetic features available in iSAT and an approximating approach. Our experimental results show that the dedicated and the compositional approach clearly outperform the approximating approach. Throughout all our benchmarks, the dedicated approach showed an equal or better performance compared to the compositional approach.Comment: In Proceedings SNR 2021, arXiv:2207.0439

    Fast Design Space Exploration of Nonlinear Systems: Part I

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    System design tools are often only available as blackboxes with complex nonlinear relationships between inputs and outputs. Blackboxes typically run in the forward direction: for a given design as input they compute an output representing system behavior. Most cannot be run in reverse to produce an input from requirements on output. Thus, finding a design satisfying a requirement is often a trial-and-error process without assurance of optimality. Finding designs concurrently satisfying multiple requirements is harder because designs satisfying individual requirements may conflict with each other. Compounding the hardness are the facts that blackbox evaluations can be expensive and sometimes fail to produce an output due to non-convergence of underlying numerical algorithms. This paper presents CNMA (Constrained optimization with Neural networks, MILP solvers and Active Learning), a new optimization method for blackboxes. It is conservative in the number of blackbox evaluations. Any designs it finds are guaranteed to satisfy all requirements. It is resilient to the failure of blackboxes to compute outputs. It tries to sample only the part of the design space relevant to solving the design problem, leveraging the power of neural networks, MILPs, and a new learning-from-failure feedback loop. The paper also presents parallel CNMA that improves the efficiency and quality of solutions over the sequential version, and tries to steer it away from local optima. CNMA's performance is evaluated for seven nonlinear design problems of 8 (2 problems), 10, 15, 36 and 60 real-valued dimensions and one with 186 binary dimensions. It is shown that CNMA improves the performance of stable, off-the-shelf implementations of Bayesian Optimization and Nelder Mead and Random Search by 1%-87% for a given fixed time and function evaluation budget. Note, that these implementations did not always return solutions.Comment: 14 pages, 26 figures. arXiv admin note: text overlap with arXiv:2010.0984

    A study of pattern recovery in recurrent correlation associative memories

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    In this paper, we analyze the recurrent correlation associative memory (RCAM) model of Chiueh and Goodman. This is an associative memory in which stored binary memory patterns are recalled via an iterative update rule. The update of the individual pattern-bits is controlled by an excitation function, which takes as its arguement the inner product between the stored memory patterns and the input patterns. Our contribution is to analyze the dynamics of pattern recall when the input patterns are corrupted by noise of a relatively unrestricted class. We make three contributions. First, we show how to identify the excitation function which maximizes the separation (the Fisher discriminant) between the uncorrupted realization of the noisy input pattern and the remaining patterns residing in the memory. Moreover, we show that the excitation function which gives maximum separation is exponential when the input bit-errors follow a binomial distribution. Our second contribution is to develop an expression for the expectation value of bit-error probability on the input pattern after one iteration. We show how to identify the excitation function which minimizes the bit-error probability. However, there is no closed-form solution and the excitation function must be recovered numerically. The relationship between the excitation functions which result from the two different approaches is examined for a binomial distribution of bit-errors. The final contribution is to develop a semiempirical approach to the modeling of the dynamics of the RCAM. This provides us with a numerical means of predicting the recall error rate of the memory. It also allows us to develop an expression for the storage capacity for a given recall error rate
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