Neural networks for Medical decision. Reti Neurali per la diagnosi in medicina

Presentazione del modello biologico del neurone, modello artificiale affine Funzioni di attivazione e composizione di reti a partire da singolo neurone Presentazione di metodologie per addestrare la rete, con processo di apprendimento analizzato: error correction e supervised. Presentazione algoritmo specifico back-propagation e suo uso per classificazione Applicazione rete neurale in campo medico, per classificare pazienti colpiti da infarto non acuto e valutare risk stratificatio

Air Data Sensor Fault Detection with an Augmented Floating Limiter

Although very uncommon, the sequential failures of all aircraft Pitot tubes, with the consequent loss of signals for all the dynamic parameters from the Air Data System, have been found to be the cause of a number of catastrophic accidents in aviation history. This paper proposes a robust data-driven method to detect faulty measurements of aircraft airspeed, angle of attack, and angle of sideslip. This approach first consists in the appropriate selection of suitable sets of model regressors to be used as inputs of neural network-based estimators to be used online for failure detection. The setup of the proposed fault detection method is based on the statistical analysis of the residual signals in fault-free conditions, which, in turn, allows the tuning of a pair of floating limiter detectors that act as time-varying fault detection thresholds with the objective of reducing both the false alarm rate and the detection delay. The proposed approach has been validated using real flight data by injecting artificial ramp and hard failures on the above sensors. The results confirm the capabilities of the proposed scheme showing accurate detection with a desirable low level of false alarm when compared with an equivalent scheme with conventional “a priori set” fixed detection thresholds. The achieved performance improvement consists mainly in a substantial reduction of the detection time while keeping desirable low false alarm rates

Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks

The values of a given manipulator's dynamics coefficients need to be accurately identified in order to employ model-based algorithms in the control of its motion. This thesis details the development of a novel form of adaptive network which is capable of accurately learning the coefficients of systems, such as manipulator inverse dynamics, where the algebraic form is known but the coefficients' values are not. Empirical motion data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear Combiner (CSLC) network developed, and the coefficients of their inverse dynamics identified. The resultant precision of control is shown to be superior to that achieved from employing dynamics coefficients derived from direct measurement. As part of the development of the CSLC network, the process of network learning is examined. This analysis reveals that current network architectures for processing analogue output systems with high input order are highly unlikely to produce solutions that are good estimates throughout the entire problem space. In contrast, the CSLC network is shown to generalise intrinsically as a result of its structure, whilst its training is greatly simplified by the presence of only one minima in the network's error hypersurface. Furthermore, a fine-tuning algorithm for network training is presented which takes advantage of the CSLC network's single adaptive layer structure and does not rely upon gradient descent of the network error hypersurface, which commonly slows the later stages of network training

Personalized Health Monitoring Using Evolvable Block-based Neural Networks

This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques

Comparative study on the performance of Au/F-TiO2 photocatalyst synthesized from Zamzam water and distilled water under blue light irradiation

Recurring problems of titanium dioxide (TiO2) for needing UV light to be activated and high electron-hole recombination rate limit the application of TiO2 as a prolific photocatalyst. By modifying the morphology and introducing electron trapping species into TiO2, the photocatalytic activity of TiO2 could be improved. Solvents of two different kinds; distilled water and Zamzam water were used in peroxotitanic acid synthesis of TiO2 and the photocatalyst was utilized to degrade Reactive Blue 19 (RB19) dye under blue light irradiation (475 nm) to assess the visible light activity of synthesized TiO2. Fluorine was incorporated to control the morphology while gold nanoparticles (GNP) stabilized by arabic gum were deposited to trap electrons. The morphology of F-TiO2 which appeared to be in ovoid shape was confirmed by Field Emission-Scanning Electron Microscope (FE-SEM) and Transmission Electron Microscope (TEM). Brunauer-Emmett-Teller (BET) surface area and crystallite size estimated from X-ray Diffraction (XRD) data revealed that F-TiO2 modified using HF was smaller in size and exhibited single anatase phase. The band gap of Au-TiO2 synthesized by distilled and Zamzam water was 2.78 eV and 2.89 eV respectively; shifted from 3.08 eV in blank TiO2. Peroxo Au/F-TiO2 synthesized with the incorporation of arabic gum as GNP stabilizer and HF as fluorine modifier degraded up to 49.23% of RB19 within two hours of reaction. The addition of fluorine and gold demonstrated high ability to enhance visible light activity of TiO2 with distilled water used as solvent displayed higher photocatalytic performance compared to Zamzam water

Detecting troubled-cells on two-dimensional unstructured grids using a neural network

In a recent paper [Ray and Hesthaven, J. Comput. Phys. 367 (2018), pp 166-191], we proposed a new type of troubled-cell indicator to detect discontinuities in the numerical solutions of one-dimensional conservation laws. This was achieved by suitably training an articial neural network on canonical local solution structures for conservation laws. The proposed indicator was independent of problem-dependent parameters, giving it an advantage over existing limiter-based indicators. In the present paper, we extend this approach to train a similar network capable of detecting troubled-cells on two-dimensional unstructured grids. The proposed network has a smaller architecture compared to its one-dimensional predecessor, making it computationally efficient. Several numerical results are presented to demonstrate the performance of the new indicator

Ship steering control using feedforward neural networks

One significant problem in the design of ship steering control systems is that the dynamics of the vessel change with operating conditions such as the forward speed of the vessel, the depth of the water and loading conditions etc. Approaches considered in the past to overcome these difficulties include the use of self adaptive control systems which adjust the control characteristics on a continuous basis to suit the current operating conditions. Artificial neural networks have been receiving considerable attention in recent years and have been considered for a variety of applications where the characteristics of the controlled system change significantly with operating conditions or with time. Such networks have a configuration which remains fixed once the training phase is complete. The resulting controlled systems thus have more predictable characteristics than those which are found in many forms of traditional self-adaptive control systems. In particular, stability bounds can be investigated through simulation studies as with any other form of controller having fixed characteristics. Feedforward neural networks have enjoyed many successful applications in the field of systems and control. These networks include two major categories: multilayer perceptrons and radial basis function networks. In this thesis, we explore the applicability of both of these artificial neural network architectures for automatic steering of ships in a course changing mode of operation. The approach that has been adopted involves the training of a single artificial neural network to represent a series of conventional controllers for different operating conditions. The resulting network thus captures, in a nonlinear fashion, the essential characteristics of all of the conventional controllers. Most of the artificial neural network controllers developed in this thesis are trained with the data generated through simulation studies. However, experience is also gained of developing a neuro controller on the basis of real data gathered from an actual scale model of a supply ship. Another important aspect of this work is the applicability of local model networks for modelling the dynamics of a ship. Local model networks can be regarded as a generalized form of radial basis function networks and have already proved their worth in a number of applications involving the modelling of systems in which the dynamic characteristics can vary significantly with the system operating conditions. The work presented in this thesis indicates that these networks are highly suitable for modelling the dynamics of a ship

Dynamic construction of back-propagation artificial neural networks.

by Korris Fu-lai Chung.Thesis (M.Phil.) -- Chinese University of Hong Kong, 1991.Bibliography: leaves R-1 - R-5.LIST OF FIGURES --- p.viLIST OF TABLES --- p.viiiChapter 1 --- INTRODUCTIONChapter 1.1 --- Recent Resurgence of Artificial Neural Networks --- p.1-1Chapter 1.2 --- A Design Problem in Applying Back-Propagation Networks --- p.1-4Chapter 1.3 --- Related Works --- p.1-6Chapter 1.4 --- Objective of the Research --- p.1-8Chapter 1.5 --- Thesis Organization --- p.1-9Chapter 2 --- MULTILAYER FEEDFORWARD NETWORKS (MFNs) AND BACK-PRO- PAGATION (BP) LEARNING ALGORITHMChapter 2.1 --- Introduction --- p.2-1Chapter 2.2 --- From Perceptrons to MFNs --- p.2-2Chapter 2.3 --- From Delta Rule to BP Algorithm --- p.2-6Chapter 2.4 --- A Variant of BP Algorithm --- p.2-12Chapter 3 --- INTERPRETATIONS AND PROPERTIES OF BP NETWORKSChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- A Pattern Classification View on BP Networks --- p.3-2Chapter 3.2.1 --- Pattern Space Interpretation of BP Networks --- p.3-2Chapter 3.2.2 --- Weight Space Interpretation of BP Networks --- p.3-3Chapter 3.3 --- Local Minimum --- p.3-5Chapter 3.4 --- Generalization --- p.3-6Chapter 4 --- GROWTH OF BP NETWORKSChapter 4.1 --- Introduction --- p.4-1Chapter 4.2 --- Problem Formulation --- p.4-1Chapter 4.3 --- Learning an Additional Pattern --- p.4-2Chapter 4.4 --- A Progressive Training Algorithm --- p.4-4Chapter 4.5 --- Experimental Results and Performance Analysis --- p.4-7Chapter 4.6 --- Concluding Remarks --- p.4-16Chapter 5 --- PRUNING OF BP NETWORKSChapter 5.1 --- Introduction --- p.5-1Chapter 5.2 --- Characteristics of Hidden Nodes in Oversized Networks --- p.5-2Chapter 5.2.1 --- Observations from an Empirical Study --- p.5-2Chapter 5.2.2 --- Four Categories of Excessive Nodes --- p.5-3Chapter 5.2.3 --- Why are they excessive ？ --- p.5-6Chapter 5.3 --- Pruning of Excessive Nodes --- p.5-9Chapter 5.4 --- Experimental Results and Performance Analysis --- p.5-13Chapter 5.5 --- Concluding Remarks --- p.5-19Chapter 6 --- DYNAMIC CONSTRUCTION OF BP NETWORKSChapter 6.1 --- A Hybrid Approach --- p.6-1Chapter 6.2 --- Experimental Results and Performance Analysis --- p.6-2Chapter 6.3 --- Concluding Remarks --- p.6-7Chapter 7 --- CONCLUSIONS --- p.7-1Chapter 7.1 --- Contributions --- p.7-1Chapter 7.2 --- Limitations and Suggestions for Further Research --- p.7-2REFERENCES --- p.R-lAPPENDIXChapter A.1 --- A Handwriting Numeral Recognition Experiment: Feature Extraction Technique and Sampling Process --- p.A-1Chapter A.2 --- Determining the distance d= δ2/2r in Lemma 1 --- p.A-