From Watson-Crick L systems to Darwinian P systems

Watson-Crick L systems are language generating devices making use ofWatson-Crick complementarity, a fundamental concept of DNA computing.These devices are Lindenmayer systems enriched with a trigger forcomplementarity transition: if a ``bad' string is obtained, then thederivation continues with its complement which is always a ``good'string. Membrane systems or P systems are distributed parallel computingmodels which were abstracted from the structure and the way offunctioning of living cells. In this paper, we first interpret theresults known about the computational completeness of Watson-Crick E0Lsystems in terms of membrane systems, then we introduce a related way ofcontrolling the evolution in P systems, by using the triggers not in theoperational manner (i.e., turning to the complement in a ``bad'configuration), but in a ``Darwinian' sense: if a ``bad' configurationis reached, then the system ``dies', that is, no result is obtained.The triggers (actually, the checkers) are given as finite state multisetautomata. We investigate the computational power of these P systems.Their computational completeness is proved, even for systems withnon-cooperative rules, working in the non-synchronized way, andcontrolled by only two finite state checkers; if the systems work in thesynchronized mode, then one checker for each system suffices to obtainthe computational completeness.     

Differential representations of multivariable linear systems with disturbances and polynomial matrix equationsPX + RY = WandPX + YN = V

A unified state-space approach to polynomial matrix equationsPX + R Y = W, PX + YN = Vbased on a generalization of the Wolovich-Guidorzi formula has been presented. In this paper a connection is shown between the solutions of the above equations and differential representations for multivariable linear systems with disturbances. Applications are presented for stable synthesis of certain types of compensators.     

Piecewise quadratic stability of fuzzy systems

Presents an approach to stability analysis of fuzzy systems. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The approach exploits the gain-scheduling nature of fuzzy systems and results in stability conditions that can be verified via convex optimization over linear matrix inequalities. Examples demonstrate the many improvements over analysis based on a single quadratic Lyapunov function. Special attention is given to the computational aspects of the approach and several methods to improve the computational efficiency are described     

Inversion of nonlinear time-varying systems

A procedure for inverting nonlinear systems that presents some computational advantages when applied to flexible robot arms or, more generally, to mechanical structures, is given. The procedure is presented in a general setting, for nonlinear time-varying systems. An iterative algorithm that computes a smooth controlled invariant time-varying manifold, the mathematical properties of which are illustrated and exploited, is also proposed. An application to a flexible two-link manipulator illustrates the algorithm and its computational advantages     

Accepting splicing systems

In this paper, we propose a novel approach to splicing systems, namely we consider them as accepting devices. Two ways of iterating the splicing operation and two variants of accepting splicing system are investigated. Altogether, we obtain four models, which are compared with each other as well as with the generating splicing systems from the computational power point of view. Several decision problems concerning the accepting splicing systems are discussed.     

Numerical systems analysis of multicomponent distributed systems

The paper presents the results in the construction of high-accuracy computational algorithms for the classes of partial derivative problems with discontinuous solutions, including ill-posed and eigenvalue problems. The optimal control in complex distributed systems is investigated. On the basis of the optimal control theory, explicit expressions are obtained for gradients of residual functionals to identify different parameters of multicomponent distributed systems. The possibility of using pseudoinverse matrices to solve some linear inverse problems in a finite number of arithmetic operations is considered.     

On generalized communicating P systems with minimal interaction rules

Generalized communicating P systems are purely communicating tissue-like membrane systems with communication rules which allow the movement of only pairs of objects. In this paper, we study the power of these systems in the case of eight restricted variants of communication rules. We show that seven of these restrictions lead to computational completeness, while using the remaining one the systems are able to compute only finite singletons of non-negative integers. The obtained results complete the investigations of the computational power of generalized communicating P systems and provide further examples for simple architectures with simple functioning rules which are as powerful as Turing machines.     

The computational power of membrane systems under tight uniformity conditions

We apply techniques from complexity theory to a model of biological cellular membranes known as membrane systems or P-systems. Like Boolean circuits, membrane systems are defined as uniform families of computational devices. To date, polynomial time uniformity has been the accepted uniformity notion for membrane systems. Here, we introduce the idea of using AC ⁰-uniformity and investigate the computational power of membrane systems under these tighter conditions. It turns out that the computational power of some systems is lowered from P to NL when using AC ⁰-semi-uniformity, so we argue that this is a more reasonable uniformity notion for these systems as well as others. Interestingly, other P-semi-uniform systems that are known to be lower-bounded by P are shown to retain their P lower-bound under the new tighter semi-uniformity condition. Similarly, a number of membrane systems that are known to solve PSPACE-complete problems retain their computational power under tighter uniformity conditions.     

On identification of random systems

Motivated by identification problems in biological and social sciences, this paper considers the identification of systems having inherently random parameters. Identification algorithms are presented for a class of non-linear regression models and differential systems having random coefficients. The results reveal the significant conceptual and computational differences that exist between deterministic and random identification procedures.     

Computation of optimal control for linear systems with delay

A numerical algorithm is presented to calculate an optimal control recursively for linear multivariable systems with delay. The algorithm is based on the method of steepest descent in Hilbert space. The optimal control of a multivariable system with delay for a quadratic criterion function is given by the Riccati partial differential equations. These are simultaneous partial differential equations which are difficult to solve numerically. In most computational algorithms, errors are inevitable, a vast memory is required, and a lot of computational time is needed. This makes it impractical to use available computers for these algorithms. The algorithm presented here gives optimal control effectively without a large memory requirement. It also provides a practical computational method for obtaining the optimal control of multivariable systems with delay. Several numerical computations are performed to show how the effectiveness of the algorithm compares with other methods.     

Resource-bounded sensing and planning in autonomous systems

This paper is concerned with the implications of limited computational resources and uncertainty on the design of autonomous systems. To address this problem, we redefine the principal role of sensor interpretation and planning processes. Following Agre and Chapman's plan-as-communication approach, sensing and planning are treated as computational processes that provide information to an execution architecture and thus improve the overall performance of the system. We argue that autonomous systems must be able to trade off the quality of this information with the computational resources required to produce it. Anytime algorithms, whose quality of results improves gradually as computation time increases, provide useful performance components for time-critical sensing and planning in robotic systems. In our earlier work, we introduced a compilation scheme for optimal composition of anytime algorithms. This paper demonstrates the applicability of the compilation technique to the construction of autonomous systems. The result is a flexible approach to construct systems that can operate robustly in real-time by exploiting the tradeoff between time and quality in planning, sensing and plan execution.     

An introduction to hierarchical systems theory

Hierarchical theory is a new and promising area of general systems theory. This theory deals basically with the decomposition of a system into subsystems forming a hierarchical structure and is, therefore, on method of dealing with complexity. These subsystems or infimals are coordinated by a coordinator or supremal in such a way as to obtain original system objectives. Hence, hierarchical theory is applicable to systems with a natural hierarchical structure or whose dimensionality is so high as to present computational difficulties. Thus it would appear to be particularly appropriate for use in public and societal systems problems.This paper presents a tutorial introduction to hierarchical system theory. Optimization theory is used as a vehicle for presenting the hierarchical concepts, although estimation, identification and other systems problems are also amenable to hierarchical structuring. An example is solved to demonstrate the theory and some computational considerations.     

Biotope: an integrated framework for simulating distributed multiagent computational systems

The study of distributed computational systems issues, such as heterogeneity, concurrency, control, and coordination, has yielded a number of models and architectures, which aspire to provide satisfying solutions to each of the above problems. One of the most intriguing and complex classes of distributed systems are computational ecosystems, which add an "ecological" perspective to these issues and introduce the characteristic of self-organization. Extending previous research work on self-organizing communities, we have developed Biotope, which is an agent simulation framework, where each one of its members is dynamic and self-maintaining. The system provides a highly configurable interface for modeling various environments as well as the "living" or computational entities that reside in them, while it introduces a series of tools for monitoring system evolution. Classifier systems and genetic algorithms have been employed for agent learning, while the dispersal distance theory has been adopted for agent replication. The framework has been used for the development of a characteristic demonstrator, where Biotope agents are engaged in well-known vital activities-nutrition, communication, growth, death-directed toward their own self-replication, just like in natural environments. This paper presents an analytical overview of the work conducted and concludes with a methodology for simulating distributed multiagent computational systems.     

Toric dynamical systems

Toric dynamical systems are known as complex balancing mass action systems in the mathematical chemistry literature, where many of their remarkable properties have been established. They include as special cases all deficiency zero systems and all detailed balancing systems. One feature is that the steady state locus of a toric dynamical system is a toric variety, which has a unique point within each invariant polyhedron. We develop the basic theory of toric dynamical systems in the context of computational algebraic geometry and show that the associated moduli space is also a toric variety. It is conjectured that the complex balancing state is a global attractor. We prove this for detailed balancing systems whose invariant polyhedron is two-dimensional and bounded.     

Dead beat controllability of polynomial systems: symboliccomputation approaches

State and output dead beat controllability tests for a very large class of polynomial systems with rational coefficients may be based on the quantifier elimination by partial cylindrical algebraic decomposition (QEPCAD) symbolic computation program. The method is unified for a very large class of systems and can handle one- or two-sided control constraints. Families of minimum time state/output dead beat controllers are obtained. The computational complexity of the test is prohibitive for general polynomial systems, but by constraining the structure of the system we may beat the curse of complexity. A computationally less expensive algebraic test for output dead beat controllability for a class of odd polynomial systems is presented. Necessary and sufficient conditions are given. They are still very difficult to check. Therefore, a number of easier-to-check sufficient conditions are also provided. The latter are based on the Grobner basis method and QEPCAD. It is shown on a subclass of odd polynomial systems how it is possible to further reduce the computational complexity by exploiting the structure of the system     

On the Lyapunov matrices for integral delay systems

We study Lyapunov matrices for the class of integral delay systems with constant kernel and one delay. The uniqueness and computational issues of these Lyapunov matrices for exponentially stable systems are investigated.     

Realization of linear dynamical systems

Realization theory for both time-invariant and time-variable linear systems is developed and its applicability to linear quadratic control and filtering is discussed. For time-invariant systems a review of canonical structure theory is given and various properties such as minimality and equivalence are characterized in terms of the Hankel matrix. Realization theory for such systems is then developed based on the Hankel matrix and a new computational algorithm is presented. For time-variable systems the emphasis is on obtaining physically meaningful realizations and several procedures which accomplish this are detailed. For "constant rank" systems, a generalization of the Hankel matrix approach is also presented.     

Declarative representations of multiagent systems

This paper explores the specification and semantics of multiagent problem-solving systems, focusing on the representations that agents have of each other. It provides a declarative representation for such systems. Several procedural solutions to a well-known test-bed problem are considered, and the requirements they impose on different agents are identified. A study of these requirements yields a representational scheme based on temporal logic for specifying the acting, perceiving, communicating, and reasoning abilities of computational agents. A formal semantics is provided for this scheme. The resulting representation is highly declarative, and useful for describing systems of agents solving problems reactively     

多变量系统的耦合梯度辨识算法与性能分析

针对多变量系统维数大、参数多、一般的辨识算法计算量大的问题, 基于耦合辨识概念, 推导多变量系统的耦合随机梯度算法, 利用鞅收敛定理分析算法的收敛性能. 算法的主要思想是将系统模型分解为多个单输出子系统,在子系统的递推辨识过程中, 将每个子系统的参数估计值耦合起来. 所提出算法与最小二乘算法和耦合最小二乘算法相比, 具有较少的计算量, 收敛速度可以通过引入遗忘因子得到改善. 性能分析表明了所提出算法收敛, 仿真实例验证了算法的有效性.