Extranoematic artifacts: neural systems in space and topology

During the past several decades, the evolution in architecture and engineering went through several stages of exploration of form. While the procedures of generating the form have varied from using physical analogous form-finding computation to engaging the form with simulated dynamic forces in digital environment, the self-generation and organization of form has always been the goal. this thesis further intend to contribute to self-organizational capacity in Architecture. The subject of investigation is the rationalizing of geometry from an unorganized point cloud by using learning neural networks. Furthermore, the focus is oriented upon aspects of efficient construction of generated topology. Neural network is connected with constraining properties, which adjust the members of the topology into predefined number of sizes while minimizing the error of deviation from the original form. The resulted algorithm is applied in several different scenarios of construction, highlighting the possibilities and versatility of this method

Improving the self-organizing feature map algorithm using an efficient initialization scheme

[[abstract]]It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters (i.e. the learning-rate parameter and the neighborhood set) of the algorithm. They usually have to be counteracted by the trial-and-error method; therefore, often time consuming retraining procedures have to precede before a neighborhood preserving feature amp is obtained. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Several data sets are tested to illustrate the performance of the proposed method.[[notice]]補正完

Social influence and neighbourhood effects in the health care market.

This work is intended to analyze the market for health care through a computational approach based on unsupervised neural networks. The paper provides a theoretical framework for a computational model that relies on Kohonen's self organizing maps (SOM), arranged into two layers: in the upper layer the competition dynamics of health care providers is modelled, whereas in the lower level patients behaviour is monitored. Interactions take place both vertically between the layers (in a bi-directional way), and horizontally, inside each level, exploiting neighbourhood features of SOM: signals move vertically from hospitals to patients and vice-versa, but they also spread out sideward, from patient to patient, and from hospital to hospital. The result is a new approach addressing the issue of hospital behaviour and demand mechanism modelling, which conjugates a robust theoretical implementation together with an instrument of deep graphical impact.self organizing maps; health market; adaptive behaviour; incomplete information; mixed market

Improving self-organising information maps as navigational tools: A semantic approach

Purpose - The goal of the research is to explore whether the use of higher-level semantic features can help us to build better self-organising map (SOM) representation as measured from a human-centred perspective. The authors also explore an automatic evaluation method that utilises human expert knowledge encapsulated in the structure of traditional textbooks to determine map representation quality. Design/methodology/approach - Two types of document representations involving semantic features have been explored - i.e. using only one individual semantic feature, and mixing a semantic feature with keywords. Experiments were conducted to investigate the impact of semantic representation quality on the map. The experiments were performed on data collections from a single book corpus and a multiple book corpus. Findings - Combining keywords with certain semantic features achieves significant improvement of representation quality over the keywords-only approach in a relatively homogeneous single book corpus. Changing the ratios in combining different features also affects the performance. While semantic mixtures can work well in a single book corpus, they lose their advantages over keywords in the multiple book corpus. This raises a concern about whether the semantic representations in the multiple book corpus are homogeneous and coherent enough for applying semantic features. The terminology issue among textbooks affects the ability of the SOM to generate a high quality map for heterogeneous collections. Originality/value - The authors explored the use of higher-level document representation features for the development of better quality SOM. In addition the authors have piloted a specific method for evaluating the SOM quality based on the organisation of information content in the map. © 2011 Emerald Group Publishing Limited

SOMvisua: Data Clustering and Visualization Based on SOM and GHSOM

Text in web pages is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects, which make extracting information from text very difficult. Search in text usually finds text similarities between paragraphs in documents. This paper proposes a framework for data clustering and visualization called SOMvisua. SOMvisua is based on a graph representation of data input for Self-Organizing Map (SOM) and Growing Hierarchically Self-Organizing Map (GHSOM) algorithms. In SOMvisua, sentences from an input article are represented as graph model instead of vector space model. SOM and GHSOM clustering algorithms construct knowledge from this article

Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)

Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.Damascus Universit

The Governor Architecture: Avoiding Catastrophic Forgetting in Robot Learning

The governor architecture is a new method for avoiding catatrophic forgetting in neural networks that is particularly useful in online robot learn- ing. The governor architecture uses a categorizer to identify events and excise long sequences of repetitive data that cause catastrophic forgetting in neural networks trained on robot-based tasks. We examine the performance of several variations of the governor architecture on a number of re- lated localization tasks using a simulated robot. The results show that governed networks perform far better than ungoverned networks. Governored networks are able to reliably and robustly prevent catastrophic forgetting in robot learning tasks

The Governor Architecture: Avoiding Catastrophic Forgetting in Robot Learning

The governor architecture is a new method for avoiding catatrophic forgetting in neural networks that is particularly useful in online robot learn- ing. The governor architecture uses a categorizer to identify events and excise long sequences of repetitive data that cause catastrophic forgetting in neural networks trained on robot-based tasks. We examine the performance of several variations of the governor architecture on a number of re- lated localization tasks using a simulated robot. The results show that governed networks perform far better than ungoverned networks. Governored networks are able to reliably and robustly prevent catastrophic forgetting in robot learning tasks