Forecasting with artificial neural networks: the state of the art

Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

From the Publisher:
"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems."
-- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. 
In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.
Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. 
Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Foundations of neural networks, fuzzy systems, and knowledge engineering

History of neural networks supervised learning - single-layer networks supervised learning - multilayer networks I supervised learning - multilayer networks II unsupervised learning associative models optimization methods a little math data.

Elements of artificial neural networks

https://link.springer.com/content/pdf/10.1057/jors.1979.126.pdf

Forecasting Economic Time Series

https://surface.syr.edu/cgi/viewcontent.cgi?article=1092&context=eecs_techreports

Original Contribution: Forecasting the behavior of multivariate time series using neural networks

Novel methodology and algorithms to seamlessly integrate logic synthesis and physical placement through a transformational approach are presented. Contrary to most placement algorithms that minimize a global cost function based on an abstract representation of the design, we decomposed the placement function into a set of transforms and coupled them directly with incremental timing, noise, and/or power analyzers. This coupling results in a direct and more accurate feedback on optimizations for placement actions. These placement transforms are then integrated with traditional logic synthesis transforms leading to a converging set of optimizations based on the concurrent manipulation of boolean, electrical, as well as physical data. Experimental results indicate that the proposed approach creates an efficient converging design flow that eliminates placement and synthesis iteration. It results in timing improvements, and maintains other global placement measures such as wire congestion and wire length. The flexibility of the transformational approach allows us to easily add, extend and support more sophisticated algorithms that involve critical as well as non-critical regions and target a variety of metrics including noise, yield and manufacturability:.

/pdf/transformational-placement-and-synthesis-38xrkzc4dr.pdf

Transformational placement and synthesis

An integrated circuit includes a first semiconductor chip including one or more circuits thereon performing substantially core logic functions, the first semiconductor chip including multiple signal pads for providing electrical connection to the one or more circuits. The integrated circuit further includes at least a second semiconductor chip including one or more circuits thereon performing substantially input/output interface functions, the second semiconductor chip including multiple signal pads for providing electrical connection to the one or more circuits on the first semiconductor chip. The signal pads on the second semiconductor chip are substantially aligned with and electrically connected to corresponding signal pads on the first semiconductor chip. The first and second semiconductor chips are mutually functionally dependent on one another, such that at least a portion of at least one of the one or more circuits on the first semiconductor chip utilizes at least a portion of at least one of the one or more circuits on the second semiconductor chip, and vice versa. The first and second semiconductor chips are formed using first and second semiconductor fabrication processes, respectively.

Integrated circuit architecture for reducing interconnect parasitics

List of Figures. List of Tables. About the Authors. Foreword. Preface. Symbols and Notation. 1. Introduction. 2. Electrical Testing of Faults. 3. Functional Fault Modeling and Testing. 4. Technology and Layout-Related Testing. 5. Built-In Self-Testing and Design for Testability. 6. Conclusion. A: Glossary. B: Commercial RAM Data. C: Market for RAMs. References. Index.

Kanad Chakraborty

Papers

Original Contribution: Forecasting the behavior of multivariate time series using neural networks

Transformational placement and synthesis

Integrated circuit architecture for reducing interconnect parasitics

Testing and Testable Design of High-Density Random-Access Memories

Connectionist models for part-family classifications