Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function

This chapter presents a study on improving the quality of the self-organizing map (SOM). We have synthesized the relevant research on assessing and improving the quality of SOM in recent years, and then proposed a solution to improve the quality of the feature map by adjusting parameters of the Gaussian neighborhood function. We have used quantization error and topographical error to evaluate the quality of the obtained feature map. The experiment was conducted on 12 published datasets and compared the obtained results with some other improving neighborhood function methods. The proposed method received the feature map with better quality than other solutions

Artificial Intelligence in the Path Planning Optimization of Mobile Agent Navigation

AbstractMany difficult problem solving require computational intelligence. One of the major directions in artificial intelligence consists in the development of efficient computational intelligence algorithms, like: evolutionary algorithms, and neural networks. Systems, that operate in isolation or cooperate with each other, like mobile robots could use computational intelligence algorithms for different problems/tasks solving, however in their behavior could emerge an intelligence called system's intelligence, intelligence of a system. The traveling salesman problem TSP has a large application area. It is a well-known business problem. Maximum benefits TSP, price collecting TSP have a large number of economic applications. TSP is also used in the transport logic Raja, 2012. It also has a wide range of applicability in the mobile robotic agent path planning optimization. In this paper a mobile robotic agent's path planning will be discussed, using unsupervised neural networks for the TSP solving, and from the TSP results the finding of a closely optimal path between two points in the agent's working area. In the paper a modification of the criteria function of the winner neuron selection will also be presented. At the end of the paper measurement results will be presented

Dynamics and topographic organization of recursive self-organizing maps

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographicmaps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizingmap (SOM) for processing sequential data, recursive SOM (RecSOM) (Voegtlin, 2002), as a nonautonomous dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data. © 2006 Massachusetts Institute of Technology

Mapping the state of financial stability

The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks as well as for predicting systemic financial crises. The Self-Organizing Financial Stability Map (SOFSM) enables a two-dimensional representation of a multidimensional financial stability space that allows disentangling the individual sources impacting on systemic risks. The SOFSM can be used to monitor macro-financial vulnerabilities by locating a country in the financial stability cycle: being it either in the pre-crisis, crisis, post-crisis or tranquil state. In addition, the SOFSM performs better than or equally well as a logit model in classifying in-sample data and predicting out-of-sample the global financial crisis that started in 2007. Model robustness is tested by varying the thresholds of the models, the policymaker’s preferences, and the forecasting horizons. JEL Classification: E44, E58, F01, F37, G01macroprudential supervision, prediction, Self-Organizing Map (SOM), Systemic financial crisis, systemic risk, visualization

Process Monitoring and Data Mining with Chemical Process Historical Databases

Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events

Incorporating web analysis into neural networks: An example in hopfield net searching

Neural networks have been used in various applications on the World Wide Web, but most of them only rely on the available input-output examples without incorporating Web-specific knowledge, such as Web link analysis, into the network design. In this paper, we propose a new approach in which the Web is modeled as an asymmetric Hopfield Net. Each neuron in the network represents a Web page, and the connections between neurons represent the hyperlinks between Web pages. Web content analysis and Web link analysis are also incorporated into the model by adding a page content score function and a link score function into the weights of the neurons and the synapses, respectively. A simulation study was conducted to compare the proposed model with traditional Web search algorithms, namely, a breadth-first search and a best-first search using PageRank as the heuristic. The results showed that the proposed model performed more efficiently and effectively in searching for domain-specific Web pages. We believe that the model can also be useful in other Web applications such as Web page clustering and search result ranking. © 2007 IEEE.published_or_final_versio

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Unsupervised feature learning using self-organizing maps.

In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. These approaches are known as feature learning or deep learning methods and have been successfully applied to classify scene images and recognize with high precision handwritten characters. In this thesis we show that a feature learning approach can be used to segment complex textures, a problem for a long time addressed proposing a large amount of handcrafted descriptors and local optimization strategies. We employ the SOM neural network for its ability to natively provide a set of topologically ordered features. These features allow us to obtain a highly accurate local description, even in areas characterized by a transition from one texture to another. We also show that a single feature learning unit can be combined with others in order to significantly improve the quality of the texture description and, consequently, reduce the segmentation errors. The results obtained prove that the proposed segmentation method is valid and provides a real alternative to other state-of-the-art methods. Since the proposed framework is simple, we easily combined it with a pyramidal histogram encoding and a SVM supervised network in order to classify scene images. We show that the important topological ordering property, inherited from the SOM network, allow us to resize the feature set, obtained during the initial unsupervised learning, avoiding an unpredictable performance loss. Moreover, the results on the standard Caltech-101 dataset prove a significant improvement on some state-of-the-art computer vision methods, designed specifically for image classification