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Neocybernetics in biological systems
, 2006
"... This report summarizes ten levels of abstraction that together span the continuum from the most elementary to the most general levels when modeling biological systems. It is shown how the neocybernetic principles can be seen as the key to reaching a holistic view of complex processes in general. Pre ..."
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This report summarizes ten levels of abstraction that together span the continuum from the most elementary to the most general levels when modeling biological systems. It is shown how the neocybernetic principles can be seen as the key to reaching a holistic view of complex processes in general. Preface Concrete examples help to understand complex systems. In this report, the key point is to illustrate the basic mechanisms and properties of neocybernetic system models. Good visualizations are certainly needed. It is biological systems, or living systems, that are perhaps the most characteristic examples of cybernetic systems. This intuition is extended here to natural systems in general — indeed, it is all other than manmade ones that seem to be cybernetic. The word “biological ” in the title should be interpreted as “biological ” — referring to general studies of any living systems, independent of the phenosphere. Starting from the concrete examples, connections to more abstract systems are found, and the discussions become more and more allembracing in this text. However, the neocybernetic model framework still makes it possible to conceptually master the complexity. There is more information about neocybernetics available in Internet — also this report is available there in electronic form:
Information and Entropy in Cybernetic Systems
"... Abstract. It has been shown that the cybernetic approaches can efficiently be used for analysis and design of complex networked systems. Still, the earlier discussions were bound to the actual application domain at hand. This paper gives more intuition in what truly takes place in a cybernetic syste ..."
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Abstract. It has been shown that the cybernetic approaches can efficiently be used for analysis and design of complex networked systems. Still, the earlier discussions were bound to the actual application domain at hand. This paper gives more intuition in what truly takes place in a cybernetic system from another point of view. Information theory, and specially the concept of entropy, offer a yet more general perspective to such analyses. 1
Preface
, 2006
"... Abstract: This report summarizes ten levels of abstraction that together span the continuum from the most elementary to the most general levels when modeling biological systems. It is shown how the neocybernetic principles can be seen as the key to reaching a holistic view of complex processes in ge ..."
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Abstract: This report summarizes ten levels of abstraction that together span the continuum from the most elementary to the most general levels when modeling biological systems. It is shown how the neocybernetic principles can be seen as the key to reaching a holistic view of complex processes in general.
ClosedLoop Subspace System Identification
, 1996
"... In this paper we present a general framework for closedloop subspace system identification. This framework consists of two new projection theorems which allow the extraction of nonsteady state Kalman filter states and of system related matrices directly from closedloop input output data. Three alg ..."
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In this paper we present a general framework for closedloop subspace system identification. This framework consists of two new projection theorems which allow the extraction of nonsteady state Kalman filter states and of system related matrices directly from closedloop input output data. Three algorithms for the identification of the state space matrices are then presented. The similarities between the theorems and algorithms, and the corresponding openloop theorems and algorithms in the literature are emphasized. The closedloop theory is illustrated with a simulation example. r u y k k P(z) C(z) k v k w k Figure 1: Standard feedback setup. u k is the input signal, y k the output signal and r k the reference signal. v k (measurement noise) and w k (process noise) are the disturbances acting on the linear plant P (z). The linear controller is represented by C(z). In this paper we assume that u k and y k are measured and that a limited number of impulse response samples (Mar...
Clinical Time Series Prediction: Towards A Hierarchical Dynamical System Framework
, 2014
"... Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of patient state, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and deve ..."
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Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of patient state, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series that combines advantages of the two approaches: Linear Dynamical Systems (LDS) and Gaussian Processes (GP). The new framework is more flexible than the two approaches individually in that it can model and learn from time series data of varied length and with irregularly sampled observations. We test our framework on the problem of learning time series models for the complete blood count panel, and show that it outperforms the existing models in terms of its predictive accuracy.
1Minimal Realization Problems for Hidden Markov Models
"... Abstract—Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM). Given the joint probabilities of finite length strings of the process, we are interested in finding a finite state generative mod ..."
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Abstract—Consider a stationary discrete random process with alphabet size d, which is assumed to be the output process of an unknown stationary Hidden Markov Model (HMM). Given the joint probabilities of finite length strings of the process, we are interested in finding a finite state generative model to describe the entire process. In particular, we focus on two classes of models: HMMs and quasiHMMs, which is a strictly larger class of models containing HMMs. In the main theorem, we show that if the random process is generated by an HMM of order less or equal than k, and whose transition and observation probability matrix are in general position, namely almost everywhere on the parameter space, both the minimal quasiHMM realization and the minimal HMM realization can be efficiently computed based on the joint probabilities of all the length N strings, for N> 4dlogd(k)e+1. In this paper, we also aim to compare and connect the two lines of literature: realization theory of HMMs, and the recent development in learning latent variable models with tensor decomposition techniques. I.
Subspace Identification for Industrial Processes
"... Abstract. Subspace identification has been a topic of research along the last years. Methods as MOESP and N4SID are well known and they use the LQ decomposition of certain matrices of input and output data. Based on these methods, it is introduced the MON4SID method, which uses the techniques MOESP ..."
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Abstract. Subspace identification has been a topic of research along the last years. Methods as MOESP and N4SID are well known and they use the LQ decomposition of certain matrices of input and output data. Based on these methods, it is introduced the MON4SID method, which uses the techniques MOESP and N4SID. Palavraschave. Subspace identification, state space models. 1.
Subspace Identification for Industrial Processes
"... Abstract. Subspace identification has been a topic of research along the last years. Methods as MOESP and N4SID are well known and they use the LQ decomposition of certain matrices of input and output data. Based on these methods, it is introduced the MON4SID method, which uses the techniques MOESP ..."
Abstract
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Abstract. Subspace identification has been a topic of research along the last years. Methods as MOESP and N4SID are well known and they use the LQ decomposition of certain matrices of input and output data. Based on these methods, it is introduced the MON4SID method, which uses the techniques MOESP and N4SID. Palavraschave. Subspace identification, state space models. 1.