Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

Aim Fuzzy wavelet neural network (FWNN) has proven to be a promising strategy in the identification of nonlinear systems. The network considers both global and local properties, deals with imprecision present in sensory data, leading to desired precisions. In this paper, we proposed a new FWNN model nominated “Fuzzy Jump Wavelet Neural Network” (FJWNN) for identifying dynamic nonlinear-linear systems, especially in practical applications. Methods The proposed FJWNN is a fuzzy neural network model of the Takagi-Sugeno-Kang type whose consequent part of fuzzy rules is a linear combination of input regressors and dominant wavelet neurons as a sub-jump wavelet neural network. Each fuzzy rule can locally model both linear and nonlinear properties of a system. The linear relationship between the inputs and the output is learned by neurons with linear activation functions, whereas the nonlinear relationship is locally modeled by wavelet neurons. Orthogonal least square (OLS) method and genetic algorithm (GA) are respectively used to purify the wavelets for each sub-JWNN. In this paper, fuzzy rule induction improves the structure of the proposed model leading to less fuzzy rules, inputs of each fuzzy rule and model parameters. The real-world gas furnace and the real electromyographic (EMG) signal modeling problem are employed in our study. In the same vein, piecewise single variable function approximation, nonlinear dynamic system modeling, and Mackey–Glass time series prediction, ratify this method superiority. The proposed FJWNN model is compared with the state-of-the-art models based on some performance indices such as RMSE, RRSE, Rel ERR%, and VAF%. Results The proposed FJWNN model yielded the following results: RRSE (mean±std) of 10e-5±6e-5 for piecewise single-variable function approximation, RMSE (mean±std) of 2.6–4±2.6e-4 for the first nonlinear dynamic system modelling, RRSE (mean±std) of 1.59e-3±0.42e-3 for Mackey–Glass time series prediction, RMSE of 0.3421 for gas furnace modelling and VAF% (mean±std) of 98.24±0.71 for the EMG modelling of all trial signals, indicating a significant enhancement over previous methods. Conclusions The FJWNN demonstrated promising accuracy and generalization while moderating network complexity. This improvement is due to applying main useful wavelets in combination with linear regressors and using fuzzy rule induction. Compared to the state-of-the-art models, the proposed FJWNN yielded better performance and, therefore, can be considered a novel tool for nonlinear system identificationPeer ReviewedPostprint (published version

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Extending BIM for air quality monitoring

As we spend more than 90% of our time inside buildings, indoor environmental quality is a major concern for healthy living. Recent studies show that almost 80% of people in European countries and the United States suffer from SBS (Sick Building Syndrome), which affects physical health, productivity and psychological well-being. In this context, environmental quality monitoring provides stakeholders with crucial information about indoor living conditions, thus facilitating building management along its lifecycle, from design, construction and commissioning to usage, maintenance and end-of-life. However, currently available modelling tools for building management remain limited to static models and lack integration capacities to efficiently exploit environmental quality monitoring data. In order to overcome these limitations, we designed and implemented a generic software architecture that relies on accessible Building Information Model (BIM) attributes to add a dynamic layer that integrates environmental quality data coming from deployed sensors. Merging sensor data with BIM allows creation of a digital twin for the monitored building where live information about environmental quality enables evaluation through numerical simulation. Our solution allows accessing and displaying live sensor data, thus providing advanced functionality to the end-user and other systems in the building. In order to preserve genericity and separation of concerns, our solution stores sensor data in a separate database available through an application programming interface (API), which decouples BIM models from sensor data. Our proof-of-concept experiments were conducted with a cultural heritage building located in Bled, Slovenia. We demonstrated that it is possible to display live information regarding environmental quality (temperature, relative humidity, CO2, particle matter, light) using Revit as an example, thus enabling end-users to follow the conditions of their living environment and take appropriate measures to improve its quality.Pages 244-250

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Function value-based multi-objective optimisation of reheating furnace operations using Hooke-Jeeves algorithm

Improved thermal efficiency in energy-intensive metal-reheating furnaces has attracted much attention recently in efforts to reduce both fuel consumption, and CO2 emissions. Thermal efficiency of these furnaces has improved in recent years (through the installation of regenerative or recuperative burners), and improved refractory insulation. However, further improvements can still be achieved through setting up reference values for the optimal set-point temperatures of the furnaces. Having a reasonable expression of objective function is of particular importance in such optimisation. This paper presents a function value-based multi-objective optimisation where the objective functions, which address such concerns as discharge temperature, temperature uniformity, and specific fuel consumption, are dependent on each other. Hooke-Jeeves direct search algorithm (HJDSA) was used to minimise the objective functions under a series of production rates. The optimised set-point temperatures were further used to construct an artificial neural network (ANN) of set-point temperature in each control zone. The constructed artificial neural networks have the potential to be incorporated into a more advanced control solution to update the set-point temperatures when the reheating furnace encounters a production rate change. The results suggest that the optimised set-point temperatures can highly improve heating accuracy, which is less than 1 °C from the desired discharge temperature

PID controller tuning for a multivariable glass furnace process by genetic algorithm

Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction. © 2015 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelber

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

A Study of the Prediction of Ammonium Bisulfate Formation Temperature by Artificial Intelligence

Ammonium bisulfate (ABS) is an acidic deposit that can form on the metal elements of air preheaters in power boilers, leading to unit operational issues. As a byproduct of the Selective Catalytic Reduction (SCR) systems for nitrogen oxide (NOx) emissions control, ABS could result in unit efficiency deterioration, even unit outage. ABS formation temperature is an important factor in controlling the issues associated with ABS fouling problems. If the ABS formation temperature could be monitored, the ABS deposition location could be identified. Subsequently, preventative actions could be taken to avoid ABS fouling to develop into a serious operational problem, such as air preheater plugging. This study deals with indirect predictive models of ABS formation temperature. Five models were developed based on data mining technologies, using actual power plant data. Data composed of 14,230 samples, from 49 variables were used in the study. In the modeling, Principal Component Analysis (PCA) and Sensitivity Analysis (SA) were used to reduce the number of variables in the data set. K-Means Clustering (KMC) was also employed to compress training samples. Neural Networks (NN) and Support Vector Machine (SVM) were used for data modeling. Model results were validated with ABS formation temperatures measured with an ABS dew-point probe. A SA was performed to determine the impact of individual variables on the ABS formation process. It was found that four unit variables: SO2 stack concentration, SCR gas outlet temperature, SCR inlet NOx concentration and dilution skid ammonia flow, can provide a good representation of the data set for ABS formation temperature prediction. The most accurate predictive model consists of a sequence of KMC and SVM. This approach can predict ABS formation temperature within a 9% error from the physical measurement