A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

History, an Exit Strategy: The Retrofuture Fabulations of kara lynch

A cartographer constructs a map of an individual creative history, that of the American artist kara lynch, as it emerges in connection to a collective history of African American cultural expression. Positioning history as complex, dynamic systems of interwoven memory networks, the map follows lynch’s traversals through various “zones of cultural haunting”: places where collective memories made invisible through systematic processes of cultural erasure may be recovered and revived. Through these traversals, which are inspired by lynch’s “forever project” Invisible, the map covers such terrains as haunted narratives, mechanisms of abstraction and coding within African American media production, water as an informational technology, the distribution of memory in blood, the dialectics of materiality and immateriality that frame considerations of black subjectivity, and the possibility that place of music might not be the site of sound but instead the social production of memory.

Automated nonlinear system modelling with multiple neural networks

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method.

Geographies of Communality, Colonialism, and Capitalism: Ecology and the World-System

Drawing upon recent reworkings of world systems theory and Marx’s concept of metabolic rift, this paper attempts to ground early nineteenth-century Ireland more clearly within these metanarratives, which take the historical-ecological dynamics of the development of capitalism as their point of departure. In order to unravel the socio-spatial complexities of Irish agricultural production throughout this time, attention must be given to the prevalence of customary legal tenure, institutions of communal governance, and their interaction with the colonial apparatus, as an essential feature of Ireland’s historical geography often neglected by famine scholars. This spatially differentiated legacy of communality, embedded within a country-wide system of colonial rent, and burgeoning capitalist system of global trade, gave rise to profound regional differentiations and ecological contradictions, which became central to the distribution of distress during the Great Famine (1845-1852). Contrary to accounts which depict it as a case of discrete transition from feudalism to capitalism, Ireland’s pre-famine ecology must be understood through an analysis which emphasises these socio-spatial complexities. Consequently, this structure must be conceptualised as one in which communality, colonialism, and capitalism interact dynamically, and in varying stages of development and devolution, according to space and time.

Dynamics of the entanglement spectrum in spin chains

We study the dynamics of the entanglement spectrum, that is the time evolution of the eigenvalues of the reduced density matrices after a bipartition of a one-dimensional spin chain. Starting from the ground state of an initial Hamiltonian, the state of the system is evolved in time with a new Hamiltonian. We consider both instantaneous and quasi adiabatic quenches of the system Hamiltonian across a quantum phase transition. We analyse the Ising model that can be exactly solved and the XXZ for which we employ the time-dependent density matrix renormalisation group algorithm. Our results show once more a connection between the Schmidt gap, i.e. the difference of the two largest eigenvalues of the reduced density matrix and order parameters, in this case the spontaneous magnetisation.

Belief change with noisy sensing in the situation calculus

Situation calculus has been applied widely in arti?cial intelligence to model and reason about actions and changes in dynamic systems. Since actions carried out by agents will cause constant changes of the agents’ beliefs, how to manage
these changes is a very important issue. Shapiro et al. [22] is one of the studies that considered this issue. However, in this framework, the problem of noisy sensing, which often presents in real-world applications, is not considered. As a
consequence, noisy sensing actions in this framework will lead to an agent facing inconsistent situation and subsequently the agent cannot proceed further. In this paper, we investigate how noisy sensing actions can be handled in iterated
belief change within the situation calculus formalism. We extend the framework proposed in [22] with the capability of managing noisy sensings. We demonstrate that an agent can still detect the actual situation when the ratio of noisy sensing actions vs. accurate sensing actions is limited. We prove that our framework subsumes the iterated belief change strategy in [22] when all sensing actions are accurate. Furthermore, we prove that our framework can adequately handle belief introspection, mistaken beliefs, belief revision and belief update even with noisy sensing, as done in [22] with accurate sensing actions only.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

Dynamic mechanical analysis of polymeric systems of pharmaceutical and biomedical significance

Dynamic mechanical analysis (DMA) is an analytical technique in which an oscillating stress is applied to a sample and the resultant strain measured as functions of both oscillatory frequency and temperature. From this, a comprehensive knowledge of the relationships between the various viscoelastic parameters, e.g. storage and loss moduli, mechanical damping parameter (tan delta), dynamic viscosity, and temperature may be obtained. An introduction to the theory of DMA and pharmaceutical and biomedical examples of the use of this technique are presented in this concise review. In particular, examples are described in which DMA has been employed to quantify the storage and loss moduli of polymers, polymer damping properties, glass transition temperature(s), rate and extent of curing of polymer systems, polymer-polymer compatibility and identification of sol-gel transitions. Furthermore, future applications of the technique for the optimisation of the formulation of pharmaceutical and biomedical systems are discussed. (C) 1999 Elsevier Science B.V. All rights reserved.

Dynamic, in situ optical, magnetic and magneto-optical monitoring of the growth of Co/Au and Pd/Co/Au multilayer systems

The growth of magnetron sputtered Co/Au and Pd/Co/Au superlattices on Au and Pd buffer layers, deposited onto glass substrates, has been monitored optically and magneto-optically in real time, using rotating analyser ellipsometry and Kerr polarimetry, at a wavelength of 633 nm. The magneto-optical traces, combined with ex situ and in situ hysteresis loops, provide a detailed and informative fingerprint of the optical and magnetic properties of the films as they evolve during growth. For Co/Au, oscillations in the polar magneto-optical effect developed during the deposition of An overlayers on Co and these may be attributed to quantum well states. However, the hysteresis measurements show that the magnetic field required to maintain saturation magnetization throughout the experiment was larger than available in situ, introducing a degree of confusion concerning the interpretation of the data. This problem was overcome by the incorporation of Pd layers into the Co/Au structure, thereby eliminating variation in magnetic orientation during growth of the Au layers as a contributory factor to the observations.

Statistical theory of finite Fermi systems with chaotic excited eigenstates

A theory of strongly interacting Fermi systems of a few particles is developed. At high excit at ion energies (a few times the single-parti cle level spacing) these systems are characterized by an extreme degree of complexity due to strong mixing of the shell-model-based many-part icle basis st at es by the residual two- body interaction. This regime can be described as many-body quantum chaos. Practically, it occurs when the excitation energy of the system is greater than a few single-particle level spacings near the Fermi energy. Physical examples of such systems are compound nuclei, heavy open shell atoms (e.g. rare earths) and multicharged ions, molecules, clusters and quantum dots in solids. The main quantity of the theory is the strength function which describes spreading of the eigenstates over many-part icle basis states (determinants) constructed using the shell-model orbital basis. A nonlinear equation for the strength function is derived, which enables one to describe the eigenstates without diagonalization of the Hamiltonian matrix. We show how to use this approach to calculate mean orbital occupation numbers and matrix elements between chaotic eigenstates and introduce typically statistical variable s such as t emperature in an isolated microscopic Fermi system of a few particles.

Dynamic Linear Precoding for the Exploitation of Known Interference in MIMO Broadcast Systems

This paper introduces a novel channel inversion (CI) precoding scheme for the downlink of phase shift keying (PSK)-based multiple input multiple output (MIMO) systems. In contrast to common practice where knowledge of the interference is used to eliminate it, the main idea proposed here is to use this knowledge to glean benefit from the interference. It will be shown that the system performance can be enhanced by exploiting some of the existent inter-channel interference (ICI). This is achieved by applying partial channel inversion such that the constructive part of ICI is preserved and exploited while the destructive part is eliminated by means of CI precoding. By doing so, the effective signal to interference-plus-noise ratio (SINR) delivered to the mobile unit (MU) receivers is enhanced without the need to invest additional transmitted signal power at the MIMO base station (BS). It is shown that the trade-off to this benefit is a minor increase in the complexity of the BS processing. The presented theoretical analysis and simulations demonstrate that due to the SINR enhancement, significant performance and throughput gains are offered by the proposed MIMO precoding technique compared to its conventional counterparts.