Unipolar terminal-attractor-based neural associative memory with adaptive threshold and perfect convergence

A perfectly convergent unipolar neural associative-memory system based on nonlinear dynamical terminal attractors is presented. With adaptive setting of the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal attractors, perfect convergence is achieved. This achievement and correct retrieval are demonstrated by computer simulation. The simulations are completed (1) by exhaustive tests with all of the possible combinations of stored and test vectors in small-scale networks and (2) by Monte Carlo simulations with randomly generated stored and test vectors in large-scale networks with an M/N ratio of 4 (M is the number of stored vectors; N is the number of neurons < 256). An experiment with exclusive-oR logic operations with liquid-crystal-television spatial light modulators is used to show the feasibility of an optoelectronic implementation of the model. The behavior of terminal attractors in basins of energy space is illustrated by examples

Computational neural learning formalisms for manipulator inverse kinematics

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

Contrastive learning and neural oscillations

The concept of Contrastive Learning (CL) is developed as a family of possible learning algorithms for neural networks. CL is an extension of Deterministic Boltzmann Machines to more general dynamical systems. During learning, the network oscillates between two phases. One phase has a teacher signal and one phase has no teacher signal. The weights are updated using a learning rule that corresponds to gradient descent on a contrast function that measures the discrepancy between the free network and the network with a teacher signal. The CL approach provides a general unified framework for developing new learning algorithms. It also shows that many different types of clamping and teacher signals are possible. Several examples are given and an analysis of the landscape of the contrast function is proposed with some relevant predictions for the CL curves. An approach that may be suitable for collective analog implementations is described. Simulation results and possible extensions are briefly discussed together with a new conjecture regarding the function of certain oscillations in the brain. In the appendix, we also examine two extensions of contrastive learning to time-dependent trajectories

Neural network training by integration of adjoint systems of equations forward in time

A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically, it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved, but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. The trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies

Indirect adaptive fuzzy finite time synergetic control for power systems

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.Вступ. Бюджетні обмеження у світі, жадібному до електроенергії, змушують комунальні підприємства експлуатувати станції, що генерують, на повну потужність, а іноді і на межі стабільності. У таких різких умовах виникнення будь-якої позаштатної ситуації або збурення може призвести до виникнення критичної ситуації, що починається з погано згасаючих коливань з подальшою втратою синхронізму та нестійкістю енергосистеми. В останні десятиліття велика увага приділялася використанню додаткових сигналів, керуючих збудження, для підвищення стійкості енергосистеми. Стабілізатори енергосистеми (СЕС) служать для вироблення додаткових сигналів керування системою збудження з метою гасіння низькочастотних коливань, спричинених збуреннями навантаження або короткими замиканнями. Проблема. Адаптивні стабілізатори енергосистем були запропоновані для того, щоб адекватно справлятися з широким діапазоном робочих умов, але вони страждають від основного недоліку, що полягає в необхідності ідентифікації моделі параметрів, спостереження за станом та обчислення коефіцієнта посилення зворотного зв'язку в режимі реального часу. Енергетичні системи є нелінійними системами з конфігураціями та параметрами, які змінюються з часом, що потребує повністю нелінійної моделі та схеми адаптивного управління для практичного операційного середовища. Новий нелінійний адаптивно-нечіткий підхід, заснований на синергетичній теорії управління, розроблений для нелінійних стабілізаторів енергосистем для подолання вищезазначених проблем. Мета. Теорія синергетичного управління успішно застосовувалася під час проєктування стабілізаторів енергосистем. Це найбільш перспективний надійний метод управління, заснований на тому ж принципі інваріантності, що і в ковзному режимі управління, але без його недоліку, пов'язаного з вібрацією. У більшості своїх програм синергетичний закон управління був розроблений на основі аналізу асимптотичної стійкості, і траєкторії системи еволюціонують до заданого атрактора, що досягає рівноваги за нескінченний час. У статті подано непрямий адаптивний нечіткий синергетичний стабілізатор енергосистеми з кінцевим часом для гасіння локальних та міжзонових мод коливань енергосистем. Методологія. Пропонована конструкція регулятора заснована на адаптивному нечіткому управлінні, що поєднує синергетичну теорію управління з атрактором кінцевого часу та синтезом Ляпунова. Удосконалюючи існуючий стабілізатор адаптивної нечіткої синергетичної енергосистеми, де нечіткі системи використовуються для апроксимації динаміки невідомої системи та надійного синергетичного управління тільки для забезпечення асимптотичної стійкості замкнутої системи, запропонований метод забезпечує властивість збіжності за кінцевий час при виведенні безперервного синергетичного закону керування. Наведено аналітичні докази збіжності за кінцевий час, що підтверджують, що запропонована адаптивна схема може гарантувати обмеженість сигналів системи та отримання стійкості за кінцевий час. Результати. Працездатність пропонованого стабілізатора оцінюється для одномашинної системи з нескінченними шинами і багатомашинної енергосистеми при різних типах збурень. Результати моделювання порівнюються з результатами, отриманими за допомогою звичайного нечіткого адаптивного синергетичного регулятора

Unipolar terminal-attractor based neural associative memory with adaptive threshold

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state

A petabyte size electronic library using the N-Gram memory engine

A model library containing petabytes of data is proposed by Triada, Ltd., Ann Arbor, Michigan. The library uses the newly patented N-Gram Memory Engine (Neurex), for storage, compression, and retrieval. Neurex splits data into two parts: a hierarchical network of associative memories that store 'information' from data and a permutation operator that preserves sequence. Neurex is expected to offer four advantages in mass storage systems. Neurex representations are dense, fully reversible, hence less expensive to store. Neurex becomes exponentially more stable with increasing data flow; thus its contents and the inverting algorithm may be mass produced for low cost distribution. Only a small permutation operator would be recalled from the library to recover data. Neurex may be enhanced to recall patterns using a partial pattern. Neurex nodes are measures of their pattern. Researchers might use nodes in statistical models to avoid costly sorting and counting procedures. Neurex subsumes a theory of learning and memory that the author believes extends information theory. Its first axiom is a symmetry principle: learning creates memory and memory evidences learning. The theory treats an information store that evolves from a null state to stationarity. A Neurex extracts information data without a priori knowledge; i.e., unlike neural networks, neither feedback nor training is required. The model consists of an energetically conservative field of uniformly distributed events with variable spatial and temporal scale, and an observer walking randomly through this field. A bank of band limited transducers (an 'eye'), each transducer in a bank being tuned to a sub-band, outputs signals upon registering events. Output signals are 'observed' by another transducer bank (a mid-brain), except the band limit of the second bank is narrower than the band limit of the first bank. The banks are arrayed as n 'levels' or 'time domains, td.' The banks are the hierarchical network (a cortex) and transducers are (associative) memories. A model Neurex was built and studied. Data were 50 MB to 10 GB samples of text, data base, and images: black/white, grey scale, and high resolution in several spectral bands. Memories at td, S(m(sub td)), were plotted against outputs of memories at td-1. S(m(sub td)) was Boltzman distributed, and memory frequencies exhibited self-organized criticality (SOC); i.e., 'l/f(sup beta)' after long exposures to data. Whereas output signals from level n may be encoded with B(sub output) = O(-log(2)f(sup beta)) bits, and input data encoded with B(sub input) = O((S(td)/S(td-1))(sup n)), B(sup output)/B(sub input) is much less than 1 always, the Neurex determines a canonical code for data and it is a lossless data compressor. Further tests are underway to confirm these results with more data types and larger samples

A global integral terminal sliding mode control based on a novel reaching law for a proton exchange membrane fuel cell system

Proton exchange membrane fuel cells are devices with huge potential for renewable and clean industries due to their high efficiency and low emissions. Since the proton exchange membrane fuel cell employed in this research supplied a low output voltage, it was encouraged to use a boost converter with a designed non-linear controller to provide a suitable end-user voltage. In this paper, we proposed a novel control framework based on sliding mode control, which is a global integral sliding mode control linked with a quick reaching law that has been implemented in a commercial fuel cell system Heliocentris FC50 through a dSpace 1102 control board. We compared the strategy with a conventional sliding mode controller and an integral terminal sliding mode controller where we addressed a Lyapunov stability proof has for each structure. We contrasted the experimental outcomes where we proved the superiority of the proposed novel design in terms of robustness, convergence speed. Additionally, as the sliding mode controllers are well known by the energy consumption caused by the chattering effect, we analysed every framework in these terms. Finally, it was found that the proposed structure offered an enhancement in the energy consumption issues. Moreover, the applicability of the proposed control scheme has been demonstrated through the real time implementation over a commercial fuel cell.The authors wish to express their gratitude to the Basque Govern-ment, through the project EKOHEGAZ (ELKARTEK KK-2021/00092) , to the Diputacion Foral de alava (DFA) , through the project CONA-VANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work. The authors wish to express their gratitude to the Basque Govern-ment, through the project EKOHEGAZ (ELKARTEK KK-2021/00092) , to the Diputacion Foral de alava (DFA) , through the project CONA-VANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

Oil reservoir properties estimation using neural networks

This paper investigates the applicability as well as the accuracy of artificial neural networks for estimating specific parameters that describe reservoir properties based on seismic data. This approach relies on JPL`s adjoint operators general purpose neural network code to determine the best suited architecture. The authors believe that results presented in this work demonstrate that artificial neural networks produce surprisingly accurate estimates of the reservoir parameters