Luis F. Mingo

A hardware circuit for selecting active rules in transition P systems

This paper presents the first step in the design of a hardware circuit implementing evolution inside membranes of a transition P system. The work presented here is part of a very ambitious project: to find and to implement a hardware system, as general as possible, able to simulate P systems evolution. Due to the complexity of this generic system, the process has been divided into several stages. Each one of these stages has concrete objectives. In particular, a circuit able to determine active rules in a determined configuration for the membrane is presented here. In order to proceed in an appropriate manner, it is needed to define a data structure containing information about the initial membrane state, that is, the initial multiset of objects, the set of evolution rules and the corresponding priority relation among them. The circuit takes these data entrances and then produce as output a set of evolution rules, active rules, which are able to produce the needed changes into the system in order to make evolve it to the next configuration.

A Binary Data Structure for Membrane Processors: Connectivity Arrays

This paper defines membrane processors as digital processors capable of implementing local processing performed inside membranes in transition P systems. In order to encode the membrane structures of transition P systems, additional binary data structures are needed. Such binary data structures are named ”connectivity arrays”. The main purposes of the connectivity arrays are to distribute information about the system membrane structure among the different processors that implement the transition P system, and to facilitate communication processes among different membrane processors. The information kept in processor has to be versatile and compact; versatile means to easily permit changes in the membrane structure of the system processors, and compact means that not too much memory space is needed.

Structures and Bio-language to Simulate Transition P Systems on Digital Computers

The aim of this paper is to show that some computational models inspired from biological membranes, such as P systems, can be simulated on digital computers in an efficient manner. To this aim, it is necessary to characterize non-determinism and parallel execution of evolution rules inside regions. Both these issues are formally described here in order to obtain a feasible description in terms of data structures and operations able to be implemented in a functional programming language. Static and dynamic structures of transition P systems are formalised in order to define a bio-language to represent them. Finally, a draft of a language for describing a transition P systems is presented. It will facilitate the description of transition P systems in terms of sentences in a high level programming language; such sentences will define a program. A process of compilation will parse the program to appropriate data structures and will launch the execution of the simulation process.

Hierarchical Knowledge Representation to Approximate Functions

This paper presents a practical example of a system based on neural networks that permits to build a conceptual hierarchy. This neural system classifies an input pattern as an element of each different category or subcategory that the system has, until an exhaustive classification is obtained. The proposed neural system is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to transmit the neural activation when an external stimulus is presented to the system. Each neural network is in charge of the input pattern recognition to any prototyped class or category, and also of transmitting the activation to other neural networks to be able to continue with the classification. Therefore, the communication of the neural activation. In the system depends on the output of each one of the neural networks, so as the functional links established among the different networks to represent the underlying conceptual hierarchy

Circuit FPGA for Active Rules Selection in a Transition P System Region

P systems or Membrane Computing are a type of a distributed, massively parallel and non deterministic system based on biological membranes. These systems perform a computation through transition between two consecutive configurations. As it is well known in membrane computing, a configuration consists in a m-tuple of multisets present at any moment in the existing m regions of the system at that moment time. Transitions between two configurations are performed by using evolution rules which are in each region of the system in a non-deterministic maximally parallel manner. This article shows the development of a hardware circuit of selection of active rules in a membrane of a transition P-system. This development has been researched by using the Quartus II tool of Altera Semiconductors. In the first place, the initial specifications are defined in orfer to outline the synthesis of the circuit of active rules selection. Later on the design and synthesis of the circuit will be shown, as well as, the operation tests required to present the obtained results.

Enhanced Neural Networks and Medical Imaging

This paper shows that the application of Enhanced Neural Networks when dealing with classification problems is more powerfull than classical Multilayer Perceptrons. These enhanced networks are able to approximate any function f(x) using a n-degree polinomial defined by the weights in the connections. Also, the addition of hidden layers in the neural architecture, increases the degree of the output equation associated to output units. So, surfaces generated by these networks are really complex and theoretically they could classify any pattern set with a number n of hidden layers. Results concerning medical imaging, breast cancer diagnosis, are studied along the paper. The proposed architecture improves obtained results using classical networks, due to the implicit data transformation computed as part of the neural architecture.

Solving complex problems with a bioinspired model

Abstract Membrane systems are parallel and bioinspired systems which simulate membranes behavior when processing information. As a part of unconventional computing, P-systems are proven to be effective in solving complex problems. A software technique is presented here that obtain good results when dealing with such problems. The rules application phase is studied and updated accordingly to obtain the desired results. Certain rules are candidate to be eliminated which can make the model improving in terms of time.

Hierarchical Function Approximation with a Neural Network Model

This article presents a neural network model that permits to build a conceptual hierarchy to approximate functions over a given interval. Bio-inspired axo-axonic connections are used. In these connections the signal weight between two neurons is computed by the output of other neuron. Such arquitecture can generate polynomial expressions with lineal activation functions. This network can approximate any pattern set with a polynomial equation. This neural system classifies an input pattern as an element belonging to a category that the system has, until an exhaustive classification is obtained. The proposed model is not a hierarchy of neural networks, it establishes relationships among all the different neural networks in order to propagate the activation. Each neural network is in charge of the input pattern recognition to any prototyped category, and also in charge of transmitting the activation to other neural networks to be able to continue with the approximation. [Article copies are available for purchase from InfoSci-on-Demand.com]

Natural Computation with Connectionist Systems

This paper presents the evolution of connectionist systems that leads into the so called networks of evolutionary processors (NEPs) and it also shows a general approach to add a learning stage in NEPs. These networks have been proven to be universal models that solve NP-problems in linear time. Most usual disadvantage is that a given NEP only can solve a given problem. NEPs with learning stages can be considered as a more general model to solve several problems, and they are a superclass of NEPs. Some theorems are shown in order to state the computational power of NEPs. First of all, artificial neural networks are revisited (including multilayer perceptrons, Jordan-Elman networks and time lagged networks), then transition P systems and NEPs are shown. Finally, a model of learning in NEPs with filtered connections is proposed

Is evolutionary computation using DNA strands feasible

Until now, there have not been many attempts at using DNA strands as the technological base for evolutionary computing. This paper tries to prove that such a computing paradigm can be achieved using DNA strands and also that it seems to be the most appropriate computing paradigm when computing with DNA strands. Classical genetic algorithm operations are translated into DNA strands and DNA operations in order to implement them. This new approach will solve the inconvenience of having great amounts of DNA strands if a NP problem must be solved.