Pedro Ponce

Artificial Organic Networks

This paper introduces a novel artificial intelligence technique bio-inspired on organic chemistry: Artificial Organic Networks (AON). In fact, organic compounds present several characteristics as stability, well-formed molecules, and easily-spanning. Thus, these compounds can be taken as inspiration in which a primitive structure assures stability in itself, and then some of these structures might be mixed in order to form more complex structures in a natural and easy way, also assuring stability. In this context, there exists the opportunity to investigate and to create new organic structures, not only based on primary elements. Thus, organic chemistry knowledge and mathematical formalization are applied into this new algorithm. Moreover, Artificial Hydrocarbon Networks (AHN), particular artificial organic compounds, are also defined and implemented.

Artificial Hydrocarbon Networks Fuzzy Inference System

This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.

Adaptive noise filtering based on artificial hydrocarbon networks: An application to audio signals

Abstract Many audio signal applications are corrupted by noise. In particular, adaptive filters are frequently applied to white noise reduction in audio. Recent work provides that there exist some insights on using an artificial intelligence method called artificial hydrocarbon networks (AHNs) for filtering audio signals. Thus, the scope of this paper is to design and implement a novel approach of artificial hydrocarbon networks on adaptive filtering for audio signals. Three experiments were developed. Results demonstrate that AHNs can reduce noise from audio signals. A comparison between the proposed algorithm and a FIR-filter is also provided. The short-time objective intelligibility value (STOI) and the signal-to-noise ratio (SNR) were used for evaluation. At last, the proposed training method for finding the parameters involved in the AHN-filter can also be used in other fields of application.

The development of an artificial organic networks toolkit for LabVIEW

Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting‐edge algorithms are the time‐consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON‐TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy‐to‐use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built‐in programming event‐driven for user interfaces), to make it simple for the end‐user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so‐called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood–brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique.

A novel robust liquid level controller for coupled-tanks systems using artificial hydrocarbon networks

A fuzzy-molecular based controller for coupled-tanks systems is proposed.Performance of our controller is higher over conventional PID controllers.Our controller is robust for different discharge rate levels at the secondary tank.Tracking and robustness experiments showed a steady-state error of 1?mm, in average. This paper proposes a robust liquid-level controller for coupled-tanks systems when dealing with variable discharge rates at the secondary tank, based on a hybrid fuzzy inference system that uses artificial hydrocarbon networks at the defuzzification step, so-called fuzzy-molecular control. The design methodology of the proposed controller is presented and discussed. In addition, a case study was run over the CE105 TecQuipment coupled-tanks system in order to implement and validate the fuzzy-molecular controller proposed in that work. A comparative evaluation with the proposed controller, a conventional PID controller specifically designed for this system and a QFT robust controller, was done. Also, a performance evaluation in terms of robustness, reference-tracking in a fixed operating point and reference-tracking in a variable operating point on-the-fly was run and analyzed. Results conclude that the proposed fuzzy-molecular controller deals with uncertainty and noise, can handle dynamics in operating point, a model of the plant is not required, and it is easy and simple to implement in comparison with other controllers in literature. To this end, the proposed fuzzy-molecular liquid-level controller inherits characteristics from fuzzy controllers and artificial hydrocarbon networks in order to implement an advanced robust and intelligent control system.

A New Training Algorithm for Artificial Hydrocarbon Networks Using an Energy Model of Covalent Bonds

Abstract Computational algorithms for modeling problems are widely used in control engineering systems. Several algorithms for modeling systems are proposed in literature. However, they have some drawbacks like stability of algorithms and absence of information inside the model acting as black boxes. Recently, a novel technique called artificial hydrocarbon networks (AHNs) was proposed to improve the latter drawbacks in computational algorithms; but there is no training algorithm that specifies how to build the structure and tune all parameters involved on it. Thus, this paper introduces a new training algorithm for AHNs using an energy model of covalent bonds inspired on organic chemistry observations. Results report that this training algorithm can be used for designing the structure of AHNs while obtaining the parameter values, both at the same time.

A Novel Speed Control for DC Motors: Sliding Mode Control, Fuzzy Inference System, Neural Networks and Genetic Algorithms

DC motors have been leading the field of adjustable speed drives for a long time due to its excellent control characteristics. This paper addresses a novel speed control application for DC motors gathering the features of Sliding Mode Control (SMC), Fuzzy Inference System (FIS), Neural Networks (NNs) and Genetic Algorithms (GAs). The main goal about combining these techniques is to create a robust speed controller avoiding the main disadvantage of SMC, the chattering. The design of the controller is implemented on a FPGA (Field Programmable Gate Array) and the steps for carrying out the implementation are described in detail. Finally, the results show a comparison between three different schemes of the designed controller.

Design based on fuzzy signal detection theory for a semi-autonomous assisting robot in children autism therapy

There are different kinds of robots that are used to assist autistic children during therapy; however, there is not a previous evaluation in place to decide if the robot can detect and send social interaction clues to the child in correct manner. Since the signal detection and fuzzy signal detection theories are well known techniques in human psychology for detecting signal and noise relationships, this work proposes those techniques as a main tool to identify how effectively stimuli are detected by social robots. Unlike traditional psychophysical approaches, which treat observers as sensors, signal detection theory recognizes that observers are both sensors and decision makers, and that these are distinct processes that can be measured using separate indices, sensitivity and response criterion. Hence, the robot can be defined as an observer using the signal detection theory. This proposal allows to evaluate social robots with human psychology tools in order to improve the human-robot interaction. Thus, the robots accomplish specific social responses that can be a better approach during the autism therapy. Furthermore, the fuzzy signal detection theory (FSDT) applied to social skills can be an enhanced procedure for designing social robots. A semi-autonomous social robot was designed to validate the proposal. A novel methodology for designing social robot based on signal detection theory.The results show an effective method to improve the human robot interaction.The semiautonomous robots reach excellent results for autism therapy.

Technology transfer motivation analysis based on fuzzy type 2 signal detection theory

This paper presents a complete study based on signal detection theory (SDT) for deciding the motivation factors that motivate academic researchers to participate in the technology transfer process (university–industry relationship). Moreover, this study determines the researchers’ perception about the motivations strategies designed in universities. The paper focuses on positive motivation factors such as academic prestige, competition, generation of resources, the solution of complex problems, professional challenge, personal gains, personal gratification and the solution of society problems. The negative motivation factors studied in the paper are as follows: innovation environment, time required, and lack of incentive and fear of contravening university policies. The importance of SDT lies in the fact that it is a theory that can deal with observer perception and the ways in which choices are made. This paper proposes fuzzy sets type 2 in SDT to expand its potential and understand the decision of the researchers during the technology transfer process under conditions of uncertainty. Although fuzzy type 1 detection theory (FDT) allows signals to overlap (non-binary description), a complete representation of uncertainty is not incorporated. Thus, fuzzy type 2 signal detection theory (FDT2) is proposed to model the uncertainties and noise condition under technology transfer process. High standards of motivation can maintain and attract competent researchers at universities; thus, this paper deals in a deep fashion with all the main aspects about those motivation factors using FDT2.

Designing a S2-Enterprise (Smart x Sensing) Reference Model

The definition of the concept of S2-Enterprise (Smart x Sensing) reference model is presented. The S2 Reference Model (S2-RM) is targeted to assist in the system specification and development of future computer based systems, which support the creation of this concept. It is a hybrid reference model that includes Enterprise Integration Engineering Concepts and Interoperability models. The Reference Model for Open Distributed Processing or RM-ODP is used as the basis to define S2-RM because it allows the definition of the structure and characteristic of the Smart and Sensing systems in terms of five different views (enterprise, information, computational, engineering and technology). The importance of using reference models for the definition, design and implementation of S2-Enterprise systems is outlined. A pilot system has been developed using a micro-factory concept and a collaborative networked organization.