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This article is a summary of a three-hour discussion at Stanford University in September 2019 among the authors. It has been written with combined experiences at and with organizations such as Zilo...

This article is a summary of a three-hour discussion at Stanford University in September 2019 among the authors. It has been written with combined experiences at and with organizations such as Zilog, Altera, Xilinx, Achronix, Intel, IBM, Stanford, MIT, Berkeley, University of Wisconsin, the Technion, Fairchild, Bell Labs, Bigstream, Google, DIGITAL (DEC), SUN, Nokia, SRI, Hitachi, Silicom, Maxeler Technologies, VMware, Xerox PARC, Cisco, and many others. These organizations are not responsible for the content, but may have inspired the authors in some ways, to arrive at the colorful ride through FPGA space described above.

Fuzzy classification is one of the most important applications in fuzzy set and fuzzy-logic-related research. Its goal is to find a set of fuzzy rules that form a classification model. Most of the existing fuzzy rule induction methods (e.g., the fuzzy decision trees (FDTs) induction method) focus on searching rules consisting of triangular norms (t-norms) (i.e., and) only, but not triangular conorms (t-conorms) (or) explicitly. This may lead to the omission of generating important rules that involve t-conorms explicitly. This paper proposes a type of tree termed pattern trees (PTs) that makes use of different aggregations, including both t-norms and t-conorms. Like decision trees, PTs are an effective tool for classification applications. This paper discusses the difference between decision trees and PTs, and also shows that the subsethood-based method (SBM) and the weighted-subsethood-based method (WSBM) are two specific cases of PT induction. A novel PT induction method is proposed using similarity measure and fuzzy aggregations. The comparison to other classification methods including SBM, WSBM, C4.5, nearest neighbor, support vector machine, and FDT induction shows that: 1) PTs can obtain high accuracy rates in classifications; 2) PTs are robust to overfltting; and 3) PTs, especially simple pattern trees (SPTs), maintain compact tree structures.

Inspired by human's remarkable capability to perform a wide variety of physical and mental tasks without any measurements and computations and dissatisfied with classical logic as a tool for modeling human reasoning in an imprecise environment, Lotfi A. Zadeh developed the theory and foundation of fuzzy logic with his 1965 paper "Fuzzy sets" (Zadeh in Inf Control 8:378---53, 1965) and extended his work with his 2005 paper "Toward a generalized theory of uncertainty (GTU)--an outline" (Zadeh in Inf Control, 2005). Fuzzy logic has at least two main sources over the past century. The first of these sources was initiated by Peirce in the form what he called a logic of vagueness in 1900s, and the second source is Lotfi's A. Zadeh work, fuzzy sets and fuzzy Logic in the 1960s and 1970s.

Retrieving relevant information is a crucial component of cased-based reasoning systems for Internet applications such as search engines. The task is to use user-defined queries to retrieve useful information according to certain measures. Even though techniques exist for locating exact matches, finding relevant partial matches might be a problem. It may not be also easy to specify query requests precisely and completely - resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for large repositories such as World Wide Web, a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise or fuzzy query specification or search.

The process of ranking (scoring) has been used to make billions of financing decisions each year serving an industry worth hundreds of billion of dollars. To a lesser extent, ranking has also been used to process hundreds of millions of applications by U.S. Universities resulting in over 15 million college admissions in the year 2000 for a total revenue of over $250 billion. College admissions are expected to reach over 17 million by the year 2010 for total revenue of over $280 billion. In this paper, we will introduce fuzzy query and fuzzy aggregation as an alternative for ranking and predicting the risk for credit scoring and university admissions, which currently utilize an imprecise and subjective process. In addition we will introduce the BISC Decision Support System. The main key features of the BISC Decision Support System for the internet applications are (1) to use intelligently the vast amounts of important data in organizations in an optimum way as a decision support system and (2) to share intelligently and securely company's data internally and with business partners and customers that can be process quickly by end users.

As we approach the next millennium, and as our problems become too complex to rely only on one discipline to solve them more effectively, multi-disciplinary approaches in the petroleum industry become more of a necessity than professional curiosity. We will be forced to bring down the walls we have built around classical disciplines such as petroleum engineering, geology, geophysics and geochemistry, or at the very least, make them more permeable. Our data, methodologies and approaches to tackle problems will have to cut across various disciplines. As a result, today's “integration”, which is based on integration of results, will have to give way to a new form of integration, that is, integration of disciplines. In addition, to solve our complex problem, one needs to go beyond standard techniques and silicon hardware. The model needs to use several emerging methodologies and soft computing techniques: Expert Systems, Artificial Intelligence, Neural Network, Fuzzy Logic (GL), Genetic Algorithm (GA), Probabilistic Reasoning (PR), and Parallel Processing techniques. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, and partial truth. Soft Computing is also tractable, robust, efficient and inexpensive. In this paper, we reveal (explore) the role of Soft Computing techniques in intelligent reservoir characterization and exploration.

An integrated methodology has been developed to identify nonlinear relationships and mapping between 3-D seismic data and production log data. This methodology has been applied to a producing field. The method uses conventional techniques such as geostatistical and classical pattern recognition in conjunction with modern techniques such as soft computing (neuro-computing, fuzzy logic, genetic computing, and probabilistic reasoning). An important goal of our research is to use clustering techniques to recognize the optimal location of a new well based on 3-D seismic data and available production-log data. The classification task was accomplished in three ways; (1) k-mean clustering, (2) fuzzy c-means clustering, and (3) neural network clustering to recognize similarity cubes. Relationships between each cluster and production-log data can be recognized around the well bore and the results used to reconstruct and extrapolate production-log data away from the well bore. This advanced technique for analysis and interpretation of 3-D seismic and log data can be used to predict: (1) mapping between production data and seismic data, (2) reservoir connectivity based on multi-attribute analysis, (3) pay zone estimation, and (4) optimum well placement.

Dynamic neural network control (DNNC) is a model predictive control strategy potentially applicable to nonlinear systems. It uses a neural network to model the process and its mathematical inverse to control the process. The advantages of single hidden layer DNNC are threefold: First, the neural network structure is very simple, having limited nodes in the hidden layer and output layer for the SISO case. Second, DNNC offers potential for better initialization of weights along with fewer weights and bias terms. Third, the controller design and implementation are easier than control strategies such as conventional and hybrid neural networks without loss in performance. The objective of this paper is to present the basic concept of single hidden layer DNNC and illustrate its potential. In addition, this paper provides a detailed case study in which DNNC is applied to the nonisothermal CSTR with time varying parameters including activation energy (i.e., deactivation of catalyst) and heat transfer coefficient (i.e., fouling). DNNC is compared with PID control. Although it is clear that DNNC will perform better than PID, it is useful to compare PID with DNNC to illustrate the extreme range of the nonlinearity of the process. This paper represents a preliminary effort to design a simplified neural network-based control approach for a class of nonlinear processes. Therefore, additional work is required for investigation of the effectiveness of this approach for other chemical processes such as batch reactors. The results show excellent DNNC performance in the region where conventional PID control fails.

This article concerns non-linear control of single-input-single-output processes with input constraints and deadtimes. The problem of input-output linearization in continuous time is formulated as a model-predictive control problem, for processes with full-state measurements and for processes with incomplete state measurements and deadtimes. This model-predictive control formulation allows one (i) to establish the connections between model-predictive and input-output linearizing control methods; and (ii) to solve directly the problems of constraint handling and windup in input-output linearizing control. The derived model-predictive control laws have the shortest possible prediction horizon and explicit analytical form, and thus their implementation does not require on-line optimization. Necessary conditions for stability of the closed-loop system under the constrained dynamic control laws are given. The connections between (a) the developed control laws and (b) the model state feedback control and the modified internal model control are established. The application and performance of the derived controllers are demonstrated by numerical simulations of chemical and biochemical reactor examples.

Design techniques for non-linear dynamic systems are closely related to their stability properties. Stability results can be used to design a reliable controller. This paper discusses the stability analysis of the dynamic neural network control (DNNC). The results from DNNC stability analysis will be used to define the neural network stability index (NNSI). The NNSI is a practical index which in current form can only be used with DNNC structures. The NNSI can be used to determine the optimal DNNC network structure. In addition, we will provide guidelines for the design of an optimal DNNC network structure for the conventional neural network structure for model-based control strategies. In this study, DNNC will be designed for a non-isothermal CSTR as an example of a wide class of non-linear processes.

This article presents a continuous-time formulation of model predictive control. This formulation allows (i) to establish the connections between model predictive control and input-output linearizing control methods and (ii) to address the problems of constraint handling and windup in input-output linearizing control methods. Model predictive control laws with the shortest possible prediction horizon are derived for constrained nonlinear processes with deadtime. They have explicit analytical form, and thus their implementation does not require on-line optimization. Furthermore, in the absence of constraints, they are input-output linearizing.

Dynamic matrix control (DMC) is now widely used in industrial control, and when properly applied results in significant savings due to improved efficiency and reduced off-spec product. The objective of this paper is to implement MIMO DMC on a simulated diaphragm-type chlorine/caustic electrolyser. A comparison between the MIMO DMC and traditional multi-loop SISO PID control strategy shows greatly improved controller performance and cell efficiency using DMC, which will result in reduced operating costs and increase cell diaphragm lifespan.

Morphic Computing was first introduced by Resconi and Nikravesh [2006, 2007a, and 2007b] which makes use of Morphic and Morphogenetic Fields. Morphic Fields and its subset Morphogenetic Fields have been at the center of controversy for many years in mainstream science and the hypothesis is not accepted by some scientists who consider it a pseudoscience. Resconi and Nikravesh [2006, 2007a, and 2007b] claim that Morphic Computing is a natural extension of Holographic Computation, Quantum Computation, Soft Computing, and DNA Computing. In this paper, we introduce extensions of the Morphic Computing such as Quantum Logic and Entanglement in Morphic Computing, Morphic Systems and Morphic System of Systems (M-SOS). Its applications to the field of computation by words as an example of the Morphic Computing, Morphogenetic Fields in neural network and Morphic Computing, Morphic Fields - concepts and Web search, and agents and fuzzy in Morphic Computing will also be discussed.

Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.

Fuzzy classification is one of the most important applications of fuzzy logic. Its goal is to find a set of fuzzy rules which describe classification problems. Most of the existing fuzzy rule induction methods (e.g., the fuzzy decision trees induction method) focus on searching rules consisting of t-norms (i.e., AND) only, but not t-conorms (OR) explicitly. This may lead to the omission of generating important rules which involve t-conorms explicitly. This paper proposes a type of tree termed pattern trees which make use of different aggregations including both t-norms and t-conorms. Like decision trees, pattern trees are an effective machine learning tool for classification applications. This paper discusses the difference between decision trees and pattern trees, and also shows that the subsethood based method (SBM) and the weighted subsethood based method (WSBM) are two specific cases of pattern trees, with each having a fixed pattern tree structure.

In this paper, we introduce a new type of computation called ‘‘Morphic Computing’’. Morphic Computing is based on Field Theory and more specifically Morphic Fields. We claim that Morphic Computing is a natural extension of Holographic Computation, Quantum Computation, Soft Computing, and DNA Computing. All natural computations bonded by the Turing Machine can be formalised and extended by our new type of computation model – Morphic Computing. In this paper, we introduce the basis for this new computing paradigm.

In this overview paper, we highlight role of Soft Computing techniques for intelligent reservoir characterization and exploration, seismic data processing and characterization, well logging, reservoir mapping and engineering. Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.

A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules connected with t-conorms explicitly. Pattern trees can not only obtain high accuracy rates in classification applications, but also be robust to over-fitting. This paper further extends pattern trees approach by assigning certain weights to different trees, to reflect the nature that different trees may have different confidences. The concept of weighted pattern trees is important as it offers an option to trade off the complexity and performance of trees. In addition, it enhances the semantic meaning of pattern trees. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both of them are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that (weighted) pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined.