Neocybernetics in biological systems

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 man-made ones that seem to be cybernetic. The word “biological ” in the title should be interpreted as “bio-logical ” — 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 all-embracing 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

Self-Organizing Artificial Neural Networks in Dynamic Systems Modeling and Control

This thesis discusses the potential of self-organizing networks applied to the modeling and control of dynamic systems. A general framework is created for self-organizing networks applications that is especially suited to control engineering. Apart from the traditional time-domain features, linear operators can be mapped in the network. Efficient algorithms are derived for adapting the parameters in the self-organizing net. These algorithms are motivated as solutions to well-founded optimization problems. Two prototypical approaches to realizing controllers based on the neural network representations are presented with simple application examples. The theoretical problems of combining self-organizing networks with dynamic systems are discussed extensively. The algorithms that are derived for self-organization are not limited only to control engineering applications. On the other hand, the theoretical analyses of dynamic systems are not limited to neural networks applications--for example, the identi cation algorithms and the results achieved on the systems identifiability are applicable to the general parameter estimation tasks

On Emergent Models and Optimization of Parameters

There has been considerable interest on the “New Science ” and its promises, but very few practical evidence has been presented to motivate the complex systems hype. However, it seems that the new approaches can give new insights. This paper shows how the new conceptual tools can help also in practical control engineering tasks as in optimization of parameters. Discussions here are closely related to another paper [4]. 1. COMPLEX SYSTEMS AND EMERGENCE Assume that a set of experts has been developing a sophisticated partial differential equation model for, say, some chemical reactor. Typically, such a model is based on partial differential equations – making this kind of model useful for simulation or control design purposes, it has to be approximated. The resulting lumped parameter model will typically have dozens of free parameters that cannot be exactly determined using physical knowledge, and some kind of parameter tuning has to be carried out. Validation of such a model against actual measurements, simplifying it, and detecting the actual relevance of the individual parameters can be an extremely difficult task, and tools that could help in this task would be invaluable. As presented in [4], theory of complex systems may give new tools when searching for new tools fo

SYSTEMS THEORY VS. THEORY OF MIND: TOWARDS A SYNTHESIS? 1

Systems theory offers tools for analysis and construction of models for different kinds of systems. It turns out that these tools may open new horizons also when studying mental phenomena. This paper illustrates the new possibilities, and proposes a new view of “mental imagery ” that attacks some of the old paradoxes in the field. 1

Elastic Systems: Case of Hebbian Neurons

As the neuronal (and cognitive) processes are, after all, so well known, they offer a nice testbench for complexity theories, and they are a nice prototype for understanding behaviors in cybernetic systems in general. It turns out that when applying the framework of Hebbian neurons, many observed brain functionalities can be attacked, including sparse coding on the low level, and chunking on the higher one. Even the mysteries of causality and mind vs. matter can perhaps be given new perspectives.

Emergence and complex systems: Towards new practices for industrial automation // Intelligent processing and manufacturing of materials

There exist various complex systems theories. Common to these ideas is that they construct fancy abstractions about complexity – so fancy that it is difficult to see the underlying domain fields any more. However, there will not exist applications of the theories if the abstractions cannot be concretized. This paper tries to show that something practical can really be reached: New approaches and conceptual tools can be developed, the application field here being industrial automation systems

Preface

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

Semantic Feature Extraction: Reader-Specific Text Document Classification

An approach to automatic modeling of text documents is presented, where `semantic features' based on contextual dependencies are extracted from the textual data. The model structure has two levels first, context categories are constructed using sentences in the documents as elementary contextual units, and, second, document categories are constructed using the lower-level document analysis results as input data. Models on both of these levels are based on a feature extraction scheme, where the features can be interpreted as coordinate axes in the linear high-dimensional space. The models are adaptive, being updated according to what kind of documents have been read, so that the user-specific `profile' helps to find relevant documents that match the user's personal model. An implementation of this approach is presented, technical details are discussed, and some results when using the program are reviewed

Towards New Languages for Systems Modelling

This paper discusses what the future modeling environments could look like. To tackle with ever increasing complexity of process models, higher level of abstraction needs to be exploited. It is noticed that the most natural way to connect low-level models to high-level tools is simulation. Based on such semantic grounding, new description formalisms can perhaps be implemented. 1. NEW CHALLENGES Because of the fieldbuses, and because of the modern sensor technology, etc., the availability of the industrial processes has been enhanced considerably. There is an explosion of structureless data facing us. The problem is that there do not exist enough domain area experts that could analyze the data and rewrite the models for the processes appropriately. Automatic modeling systems would be invaluable – systems that could not only adapt the model parameters within a predetermined structural framework, but also determine the structures themselves without too much human intervention. The modeling problems are attacked by utilizing different kinds of description formalisms. One major approach is to define more and more general formalisms (like Java language) for system description: In such environments, anything can be expressed, but this means that large numbers of expressions are neede

Elastic Systems: Role of Models and Control

Neocybernetics, and specially the framework of elastic systems, gives concrete tools for formulating intuitions concerning complex systems. It can be claimed that a cybernetic system constructs a model of its environment, and it applies model-based control to eliminate variation in its environment. There exist various valuable control intuitions that are available for understanding cybernetic behaviors, including experiences with adaptive control. It turns out that even the evolutionary processes making the systems more and more complex are thermodynamically consistent when seen in the control perspective.