A New Approach for Eliminating the Spurious States in Recurrent Neural Networks

As is well known, the Convergence Theorem for the Recurrent Neural Networks, is based in Lyapunov ́s second method, which states that associated to any one given net state, there always exist a real number, in other words an element of the one dimensional Euclidean Space R, in such a way that when the state of the net changes then its associated real number decreases. In this paper we will introduce the two dimensional Euclidean space R2, as the space associated to the net, and we will define a pair of real numbers ( x, y ) , associated to any one given state of the net. We will prove that when the net change its state, then the product x ⋅ y will decrease. All the states whose projection over the energy field are placed on the same hyperbolic surface, will be considered as points with the same energy level. On the other hand we will prove that if the states are classified attended to their distances to the zero vector, only one pattern in each one of the different classes may be at the same energy level. The retrieving procedure is analyzed trough the projection of the states on that plane. The geometrical properties of the synaptic matrix W may be used for classifying the n-dimensional state- vector space in n classes. A pattern to be recognized is seen as a point belonging to one of these classes, and depending on the class the pattern to be retrieved belongs, different weight parameters are used. The capacity of the net is improved and the spurious states are reduced. In order to clarify and corroborate the theoretical results, together with the formal theory, an application is presented

Point-to-point connectivity between neuromorphic chips using address events

This paper discusses connectivity between neuromorphic chips, which use the timing of fixed-height fixed-width pulses to encode information. Address-events (log2 (N)-bit packets that uniquely identify one of N neurons) are used to transmit these pulses in real time on a random-access time-multiplexed communication channel. Activity is assumed to consist of neuronal ensembles--spikes clustered in space and in time. This paper quantifies tradeoffs faced in allocating bandwidth, granting access, and queuing, as well as throughput requirements, and concludes that an arbitered channel design is the best choice.The arbitered channel is implemented with a formal design methodology for asynchronous digital VLSI CMOS systems, after introducing the reader to this top-down synthesis technique. Following the evolution of three generations of designs, it is shown how the overhead of arbitrating, and encoding and decoding, can be reduced in area (from N to √N) by organizing neurons into rows and columns, and reduced in time (from log2 (N) to 2) by exploiting locality in the arbiter tree and in the row–column architecture, and clustered activity. Throughput is boosted by pipelining and by reading spikes in parallel. Simple techniques that reduce crosstalk in these mixed analog–digital systems are described

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Modeling and identification of power electronic converters

Nowadays, many industries are moving towards more electrical systems and components. This is done with the purpose of enhancing the efficiency of their systems while being environmentally friendlier and sustainable. Therefore, the development of power electronic systems is one of the most important points of this transition. Many manufacturers have improved their equipment and processes in order to satisfy the new necessities of the industries (aircraft, automotive, aerospace, telecommunication, etc.). For the particular case of the More Electric Aircraft (MEA), there are several power converters, inverters and filters that are usually acquired from different manufacturers. These are switched mode power converters that feed multiple loads, being a critical element in the transmission systems. In some cases, these manufacturers do not provide the sufficient information regarding the functionality of the devices such as DC/DC power converters, rectifiers, inverters or filters. Consequently, there is the need to model and identify the performance of these components to allow the aforementioned industries to develop models for the design stage, for predictive maintenance, for detecting possible failures modes, and to have a better control over the electrical system. Thus, the main objective of this thesis is to develop models that are able to describe the behavior of power electronic converters, whose parameters and/or topology are unknown. The algorithms must be replicable and they should work in other types of converters that are used in the power electronics field. The thesis is divided in two main cores, which are the parameter identification for white-box models and the black-box modeling of power electronics devices. The proposed approaches are based on optimization algorithms and deep learning techniques that use non-intrusive measurements to obtain a set of parameters or generate a model, respectively. In both cases, the algorithms are trained and tested using real data gathered from converters used in aircrafts and electric vehicles. This thesis also presents how the proposed methodologies can be applied to more complex power systems and for prognostics tasks. Concluding, this thesis aims to provide algorithms that allow industries to obtain realistic and accurate models of the components that they are using in their electrical systems.En la actualidad, el uso de sistemas y componentes eléctricos complejos se extiende a múltiples sectores industriales. Esto se hace con el propósito de mejorar su eficiencia y, en consecuencia, ser más sostenibles y amigables con el medio ambiente. Por tanto, el desarrollo de sistemas electrónicos de potencia es uno de los puntos más importantes de esta transición. Muchos fabricantes han mejorado sus equipos y procesos para satisfacer las nuevas necesidades de las industrias (aeronáutica, automotriz, aeroespacial, telecomunicaciones, etc.). Para el caso particular de los aviones más eléctricos (MEA, por sus siglas en inglés), existen varios convertidores de potencia, inversores y filtros que suelen adquirirse a diferentes fabricantes. Se trata de convertidores de potencia de modo conmutado que alimentan múltiples cargas, siendo un elemento crítico en los sistemas de transmisión. En algunos casos, estos fabricantes no proporcionan la información suficiente sobre la funcionalidad de los dispositivos como convertidores de potencia DC-DC, rectificadores, inversores o filtros. En consecuencia, existe la necesidad de modelar e identificar el desempeño de estos componentes para permitir que las industrias mencionadas desarrollan modelos para la etapa de diseño, para el mantenimiento predictivo, para la detección de posibles modos de fallas y para tener un mejor control del sistema eléctrico. Así, el principal objetivo de esta tesis es desarrollar modelos que sean capaces de describir el comportamiento de un convertidor de potencia, cuyos parámetros y/o topología se desconocen. Los algoritmos deben ser replicables y deben funcionar en otro tipo de convertidores que se utilizan en el campo de la electrónica de potencia. La tesis se divide en dos núcleos principales, que son la identificación de parámetros de los convertidores y el modelado de caja negra (black-box) de dispositivos electrónicos de potencia. Los enfoques propuestos se basan en algoritmos de optimización y técnicas de aprendizaje profundo que utilizan mediciones no intrusivas de las tensiones y corrientes de los convertidores para obtener un conjunto de parámetros o generar un modelo, respectivamente. En ambos casos, los algoritmos se entrenan y prueban utilizando datos reales recopilados de convertidores utilizados en aviones y vehículos eléctricos. Esta tesis también presenta cómo las metodologías propuestas se pueden aplicar a sistemas eléctricos más complejos y para tareas de diagnóstico. En conclusión, esta tesis tiene como objetivo proporcionar algoritmos que permitan a las industrias obtener modelos realistas y precisos de los componentes que están utilizando en sus sistemas eléctricos.Postprint (published version

From holism to compositionality: memes and the evolution of segmentation, syntax, and signification in music and language

Steven Mithen argues that language evolved from an antecedent he terms “Hmmmmm, [meaning it was] Holistic, manipulative, multi-modal, musical and mimetic”. Owing to certain innate and learned factors, a capacity for segmentation and cross-stream mapping in early Homo sapiens broke the continuous line of Hmmmmm, creating discrete replicated units which, with the initial support of Hmmmmm, eventually became the semantically freighted words of modern language. That which remained after what was a bifurcation of Hmmmmm arguably survived as music, existing as a sound stream segmented into discrete units, although one without the explicit and relatively fixed semantic content of language. All three types of utterance – the parent Hmmmmm, language, and music – are amenable to a memetic interpretation which applies Universal Darwinism to what are understood as language and musical memes. On the basis of Peter Carruthers’ distinction between ‘cognitivism’ and ‘communicativism’ in language, and William Calvin’s theories of cortical information encoding, a framework is hypothesized for the semantic and syntactic associations between, on the one hand, the sonic patterns of language memes (‘lexemes’) and of musical memes (‘musemes’) and, on the other hand, ‘mentalese’ conceptual structures, in Chomsky’s ‘Logical Form’ (LF)

Localization in GPS denied environment

No abstract available

Measurements, Models, Systems and Design

531 s.

Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)

The purpose of this Feature is to critically examine and to contribute to the burgeoning multi disciplinary literature on markets as complex adaptive systems (CAS). Three economists, Robert Axtell, Steven Durlauf and Arthur Robson who have distinguished themselves as pioneers in different aspects of how the thesis of evolutionary complexity pertains to market environments have contributed to this special issue. Axtell is concerned about the procedural aspects of attaining market equilibria in a decentralized setting and argues that principles on the complexity of feasible computation should rule in or out widely held models such as the Walrasian one. Robson puts forward the hypothesis called the Red Queen principle, well known from evolutionary biology, as a possible explanation for the evolution of complexity itself. Durlauf examines some of the claims that have been made in the name of complex systems theory to see whether these present testable hypothesis for economic models. My overview aims to use the wider literature on complex systems to provide a conceptual framework within which to discuss the issues raised for Economics in the above contributions and elsewhere. In particular, some assessment will be made on the extent to which modern complex systems theory and its application to markets as CAS constitutes a paradigm shift from more mainstream economic analysis