This report presents two further experiments over the aphid data set. The first is an eval- uation of the adaptive abilities of backpropagation of errors trained MLP and a com- parison of these capabilities with the Simple EvolvingConnectionist System (SECoS) (Watts and Kasabov, 2000). The goal of the first experiment is to compare both the performance and the adaptive abilities of the two models. The second experiment is an investigation of the sensitivity of the SECoS to the exclusion of various input vari- ables. The goal of the second experiment is to determine which of the thirteen input variables contributes the most to the modelling of the problem, that is, which variable the network is most sensitive to.
Evolvingconnectionistsystems (ECOS) are modular connectionist based systems that evolve their structure and functionality in a continuous, self-organised, on-line, adaptive, interactive way from incoming information [22-25]. They can process both data and knowledge in a supervised and/or unsupervised way . ECOS learn local models from data through clustering of the data and associating a local output function for each cluster represented in a connectionist structure. They can learn incrementally single data items or chunks of data and also incrementally change their input features [40,41]. Elements of ECOS have been proposed as part of the classical NN models, such as SOM, RBF, FuzyARTMap, Growing neural gas, neuro-fuzzy systems, RAN (see ). Other ECOS models, along with their applications, have been reported in [27,20].
[14,43] and the more general and philosophical concept of knowledge generation or discovery from the data . Both refer to the non-trivial process of identifying valid and understandable/interpretable structure in the data. In this respect system identification is meant in this paper mostly as a model structure identification rather than the more limited and practically more often used parameter identification. One can note that the parameter identification under a fixed model structure is nothing more than an adjustment, tuning and thus has obviously limitations related first of all to the choice of the model structure. Since the data streams are often non-stationary it is logical to assume the structure of the data to be also dynamic, that is, to evolve. An e-ntelligent system continuously learns new data to integrate this data with the existing models. It develops its structure and functionality continuously, always adapting and modifying its knowledge representation. The e-ntelligent system approach is demonstrated here through two system modelling techniques that the authors have introduced recently and are continuing to develop, namely the evolvingconnectionistsystems  (ECOS) and evolving fuzzy systems [12,13] (EFS).
A Connectionist Architecture for Learning to Parse A C o n n e c t i o n i s t A r c h i t e c t u r e for L e a r n i n g t o P a r s e J a m e s H e n d e r s o n a n d P e t e r L a n e D e p t o f[.]
The second is that, considered as a complex physical object, the stable activation pattern is absent in digital simulations of PDP systems. In such simulations, the activation values that compose a network’s activation pattern are typically recorded in a complex array, each of whose elements is subject to updating according to the algorithms that model the network’s activity. But this data structure is not equivalent to a pattern of activation across a real (non-simulated) PDP network. The latter is an object constructed from physically connected elements (such as neurons), each of which realizes a continuously variable physical property (such as a spiking frequency) of a certain magnitude. The former, by contrast, is a symbolic representation of such an object, in that it consists of a set of discrete symbol structures that “describes” in a numerical form the individual activation levels of a network’s constituent processing units. An activation pattern across a real network thus has a range of complex structural properties (and consequent causal powers) that are not reproduced by the data structures employed in simulations. This fact is most vividly demonstrated by the temporal asymmetries that exist between real PDP networks and their digital simulations: the simulations are notoriously slow at processing information, when compared to their real counterparts, in spite of the incredible computational speed of the digital machines on which they are run. The bottom line here is that a simulated stable pattern of activity is no more a stable activation pattern than a simulated hurricane is a hurricane. Consequently, because stable patterns of activity are absent in digital simulations of PDP systems, so are phenomenal experiences, on our account.
representations are not easily implemented in connectionistsystems. However, recent work in the connectionist modelling of analogy formation has shown how feature- based attributes may be dynamically bound to relational structure in a distributed network (Hummel & Holyoak, 1997). Such a network still exploits similarity-based processing and pattern completion in forming and retrieving analogies. Moreover, Henderson and Lane (1998) have shown that such dynamically bound representations may be learnt in a neural network architecture. We would make two claims. First, we believe that the approach of the MPC model is extendable to structured
We present a connectionist model that provides a mechanistic account of the development of simple relational analogy completion. Drawing analogies arises as a bi-product of pattern completion in a network that learns input/output pairings representing relational information. Analogy is achieved by an initial example of a relation priming the network such that the subsequent presentation of an input produces the correct analogical response. The results show that the model successfully solves simple A:B::C:D analogies and that its developmental trajectory closely parallels that of children. Finally, the model makes two strong empirical predictions.
For critical systems it is important to know whether the system is trustworthy and to be able to communicate, review and debate the level of trust achieved. In the safety domain, explicit Safety Cases are increasingly required by law, regulations and standards. Increasingly, the case is made using a goal-based approach, where claims (or goals) are made about the system and arguments and evidence are presented to support those claims. The need to understand risks is not just a safety issue: more and more organisations need to know their risks and to be able to communicate and address them to multiple stakeholders from the boardroom to back office and beyond. The type of argumentation used for safety cases is not specific to safety alone, but it can be used to justify the adequacy of systems in different applications, including security critical, business critical or service critical. An international community has begun to form around this issue of generalised assurance cases and the challenge of moving from the rhetoric to the reality of being able to implement convincing and valid cases , . The “case” and associated supporting tools can be seen as having a number of roles:
fuzzy and neuro-fuzzy models have been developed in modeling nonlinearity and time-varying structures. In an evolving model, the structure can evolve through the time based on observing samples of input and output signals. The structure of a nonlinear evolving model often can be described as an interpolated of locally linear models constructed from simple if-then fuzzy rules. There are two particularly influential works in this area of research [26,27]. Kasabov proposes an adaptive online learning algorithm as a dynamic evolving neural–fuzzy inference system (DENFIS). In this algorithm, fuzzy inference rules are created by using maximum distance clustering, which is utilized in partitioning of input space. Angelov and Filev introduce an online identification approach for the Takagi Sugeno (TS) model, where evolving clustering method along with a concept of potential is used to define the antecedent parts of the rules. This approach has been modified in  and . In , to evolve a specific form of TS Fuzzy Model, Lughofer suggests to use a modified version of vector quantization for new rule generation. Although evolving nonlinear systems have been developed successfully in simulation, approximation, classification and prediction, they are not straightforward basis for analyzing and designing control systems, In recent years, by the author and its cooperators, there is a tendency toward using and developing Evolving Linear Models (ELMs) [31,32]. An ELM can adapt and follow the variations of the nonlinear and time-varying system with agility . Moreover, it seems ELMs can fulfill facilities in control, analyze and design due to their linear forms.
In this work, we use four discrete maps that capture the natural dynamics present in a range of biological and physical systems. The logistic map, which we have already discussed, is a model of biological population growth. Depending on the value of parameter r, the system is attracted to either a fixed-point, cyclic or chaotic orbit . The baker’s map  is an archetypal model of deterministic chaos, capturing the irregular, unpredictable fractal structured behaviour that results from a process of repeated stretching and contraction—as seen when kneading bread, hence the map’s name. Arnold’s cat map  is another model of deterministic chaos which results from a geometric transformation of the unit square, and leads to interesting periodic behaviour. Chirikov’s standard map  captures the behaviour of dynamical systems with co-existing ordered and chaotic regimes. Its properties are discussed in detail in Section V-A2. The parameterised maps (the logistic map and Chirikov’s map) can be used either with an evolved fixed parameter value or with an extra input, whose current value is used to set the parameter. The latter is referred to as a tunable map, since its dynamics can be modified by the ABN during execution.
Recently, a simplified type of fuzzy rule-based system was introduced, called AnYa , which offers a new way of defining the “IF” part of the rules without defining the membership functions per variable in an explicit manner. The antecedent parts of fuzzy rules are formed upon the data clouds, which are the sets of data samples attracted around focal points. The data clouds in the AnYa system can be formed “from scratch”. The self-evolving mechanism of the data clouds is based on determination of the focal points. In the original paper, introducing AnYa , focal points were identified using eClustering  algorithm. The importance of this approach is that AnYa type of fuzzy systems can be considered as the third alternative type of fuzzy systems to Mamdani and Takagi-Sugeno type models (both of which share same type of antecedent/IF part) with a different, explicit membership function free antecedent/IF part .
This paper will adopt an incremental probabilis- tic context-free grammar (PCFG) parser (Schuler, 2009) that uses a right-corner variant of the left- corner parsing strategy (Aho and Ullman, 1972) coupled with strict memory bounds, as a model of human-like parsing. Syntax can readily be approxi- mated using simple PCFGs (Hale, 2001; Levy, 2008; Demberg and Keller, 2008), which can be easily tuned (Petrov and Klein, 2007). This paper will show that this representation can be streamlined to exploit the fact that a right-corner parse guarantees at most one expansion and at most one reduction can take place after each word is seen (see Section 2.2). The primary finding of this paper is that this prop- erty of right-corner parsing can be exploited to ob- tain a dramatic reduction in the number of random variables in a probabilistic sequence model parser (Schuler, 2009) yielding a simpler structure that more closely resembles connectionist models such as TRACE (McClelland and Elman, 1986), Short- list (Norris, 1994; Norris and McQueen, 2008), or recurrent models (Elman, 1991; Mayberry and Mi- ikkulainen, 2003) which posit functional units only for cognitively-motivated entities.
Sequencing in a Connectionist Model of Language Processing S e q u e n c i n g i n a C o n n e c t i o n i s t M o d e l o f L a n g u a g e P r o c e s s i n g I M i c h a e l G A S S E R 2 M i c h a[.]
MODULARITY IN A CONNECTIONIST MODEL OF MORPHOLOGY ACQUISITION MODULARITY IN A CONNECTIONIST MODEL MORPHOLOGY ACQUISITION M i c h a e l G a s s e r D e p a r t m e n t s of C o m p u t e r Science a n[.]
The list of weed simulation models presented in this paper is not at all intended to be exhaustive. Many other models have been developed to simulate important aspects of weeds in Australian cropping systems. These include the well-established Agricultural Production Systems Simulator (APSIM), which includes modules for simulating the growth of weeds in competition with crops (Keating et al., 2003), and Thornby and Walker’s (2009) model that integrates APSIM with a model of weed population dynamics to predict herbicide resistance evolution in northern Australia cropping systems.
Impact analysis can be expensive in terms of time and computational effort. PathImpact is lower-cost than many other impact analysis techniques due to its focus on relatively high-level execution information. For the pur- poses of this investigation we focus on procedure calls and returns. Even so, following system modifications — includ- ing both code modifications and changes made to test suites in response to those modifications — time constraints may make it difficult to re-collect the data needed by the algo- rithm. This can be the case even after only small modi- fications to code and test suites have been made. To em- ploy PathImpact cost-effectively across entire system lifetimes, techniques are needed to efficiently update the data required by the algorithm as those systems and their test suites evolve.