Differential linear repetitive processes are a distinct class of 2D continuous-discrete linear systems of both applications and systems theoretic interest. In applications, they arise in iterative learning control sch...
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Differential linear repetitive processes are a distinct class of 2D continuous-discrete linear systems of both applications and systems theoretic interest. In applications, they arise in iterative learning control schemes and in iterative solution algorithms for nonlinear dynamic optimal control algorithms based on the maximum principle. Repetitive processes cannot be analysed/controlled by direct application of the existing systems theory and hence a 'mature' systems theory must be developed for them followed (where appropriate) by onward translation into efficient controller design algorithms. This paper continues the development of the former area by developing some significant new results on the application of currently available delay differential systems theory to these processes.
Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adap...
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We describe the so-called time-reversal space-time block coding (TR-STBC) transmission scheme for communication systems with multiple transmit antennas operating over frequency-selective channels. TR-STBC can be seen ...
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We describe the so-called time-reversal space-time block coding (TR-STBC) transmission scheme for communication systems with multiple transmit antennas operating over frequency-selective channels. TR-STBC can be seen as an extension of the orthogonal space-time block codes for flat fading channels. We show that using TR-STBC, a linear filtering at the receiver can achieve an approximate decoupling of the space-time channel into scalar and independent frequency-selective channels and hence any standard maximum-likelihood sequence detector (MLSD) can be used for equalization. Numerical examples are provided to illustrate the performance of the TR-STBC transmission scheme.
The bacterial heat shock response refers to the mechanism by which bacteria react to a sudden increase in the ambient temperature of growth. The consequences of such an unmediated temperature increase at the cellular ...
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The bacterial heat shock response refers to the mechanism by which bacteria react to a sudden increase in the ambient temperature of growth. The consequences of such an unmediated temperature increase at the cellular level is the unfolding, misfolding, or aggregation of cell proteins, which threatens the life of the cell. Cells respond to the heat stress by initiating the production of heat-shock proteins whose function is to refold denatured proteins into their native states. The heat shock response, through the elevated synthesis of molecular chaperones and proteases, enables the repair of protein damage and the degradation of aggregated proteins. In a previous work (Kurata et al., 2001), we have devised a dynamic model for the heat shock response in E. coli. In the present paper, we provide a thorough discussion of the dynamical nature of this model. We use sensitivity analysis and simulation tools to illustrate the remarkable efficiency, robustness, and stability of the heat shock response system.
This paper presents an attempt to integrate attention and navigation skills in 3D embodied agents (virtual humanoids). The neural model presented has been divided in two main phases. Firstly the environment-categoriza...
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This paper presents an attempt to integrate attention and navigation skills in 3D embodied agents (virtual humanoids). The neural model presented has been divided in two main phases. Firstly the environment-categorization phase, where an online pattern recognition and categorization of the agent current sensory input data is carried out by an adaptive resonance driven self organizing neural network, which will finally simulate the agent's short term memory (STM). Secondly, the model must also learn how and when to map its current STM state into the navigation and attention motor layers of the 3D agent. We also review the world modelling and the agent vision system, and finally we present the first results extracted from two of the subsystems which conforms the complete neural model, such as, the environment categorization subsystem and the base navigation neural model.
Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main featur...
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Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main feature which makes them distinct from other classes of 2D linear systems is that information propagation in one of the two independent directions only occurs over a finite duration. This, in turn, means that a distinct systems theory must be developed for them, which can then be translated into efficient routinely applicable controller design algorithms for applications domains. In this paper, we give the first significant results on a positive realness based approach to the analysis of these processes.
In this paper, the problem of system decomposition of complex linear dynamical systems, by exploiting the similarity property, is studied. System decompositions were sought in terms of similarity hierarchical structur...
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In this paper, the problem of system decomposition of complex linear dynamical systems, by exploiting the similarity property, is studied. System decompositions were sought in terms of similarity hierarchical structures. Three new theoretical results were proved. A method for constructing the transformation has been derived. The conditions for such decomposition of complex linear systems are given.
Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the are...
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Using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rides for unseen situations. These benefits will be gained if the learning agents know the area of expertise and the expertness values of each other. In this paper, some Q-learning agents with different skills and expertness levels cooperate in learning. The agents use some criteria to judge others information and knowledge. Four expertness criterion, certainty and entropy measures are used to assign degrees of importance to others' Q-Tables. Effects of measuring these values based on their whole Q-Table, a portion of Q-Tables that reflects their proficiencies, and the states in Q-Tables on the learning quality are studied. Simple strategy sharing and two different weighted strategy-sharing methods are used to combine the acquired knowledge from different agents.
In this paper, the problems of isomorphic decomposition and controllability of a class of nonlinear systems possessing symmetries, on the basis of quotient systems, is studied. The isomorphic decomposition formations ...
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In this paper, the problems of isomorphic decomposition and controllability of a class of nonlinear systems possessing symmetries, on the basis of quotient systems, is studied. The isomorphic decomposition formations of these systems are derived. Finally, it is shown that controllability of the original systems can be determined by that of the subsystems, which are obtained through isomorphic decomposition. The corresponding sufficient and necessary conditions are given. Two new theoretical results have been proved.
In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This proble...
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In multiagent reinforcement learning, inter-agent credit assignment is a fundamental problem, since a single scalar reinforcement signal is the only reliable feedback that teams of learning agents receive. This problem is more critical in groups of independent learners with a joint task. In this research, it is assumed that a critic agent receives the environment feedback and assigns a proper credit to each agent using some measures. Three of such measures for a team of cooperative agents with a parallel and AND-type task are introduced. These measures somehow compare the agents' knowledge. One of these criteria, called normal expertness, is a non-relative measure while two other ones (certainty and relative normal expertness) are relative measure. It is experimentally shown that relative measures work better as they contain more information for the critic agent.
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