Neuro-fuzzy Logic Research Articles

Deciphering the virulence of Mycobacterium avium subsp. paratuberculosis isolates in animal macrophages using mathematical models

Understanding why pathogenic Mycobacterium avium subsp. paratuberculosis (Map) isolates cause disparate disease outcomes with differing magnitudes of severity is important in designing and implementing new control strategies. We applied a suite of mathematical models: i) general linear, ii) and neurofuzzy logic, to explain how the host of origin of several Map isolates, Map genotype, host, macrophage-based in vitro model and time post-infection contributed to the infection. A logistic growth ordinary differential equation (ODE) model was applied to estimate within macrophage growth rates for the different Map isolates. The models revealed different susceptibilities of bovine and ovine macrophages to Map infection and confirmed distinct virulence profiles for the isolates, judged by their ability to grow within macrophages. Ovine macrophages were able to internalize Map isolates more efficiently than bovine macrophages. While bovine macrophages were able to internalize Map isolates from cattle with more efficiency, ovine macrophages were more efficient in internalizing ovine isolates. Overall, Map isolates from goat and sheep grew minimally within macrophages or did not grow but were able to persist by maintaining its initial population. In contrast, the ability of the bovine isolates and the non-domesticated animal isolates to grow to higher CFU numbers within macrophages suggests that these isolates are more virulent than the sheep and goat isolates, or that these isolates are better adapted to infect domestic ruminants. Overall, our study confirms the different virulence levels for the Map isolates and susceptibility profiles of host macrophages, which is crucial in increasing our understanding of Map infection.

Adaptive neuro fuzzy determination of impactful factors on non-uniformity of polished surface

In this study, the neuro fuzzy logic selection of the most influential parameters for non-uniformity of polished surface was performed. The influence of three parameters was analyzed: carrier load, thickness and the pads elastic modulus. Adaptive neuro fuzzy logic or ANFIS was used for establishing the nonlinear relationships between the three parameters and the non-uniformity polished surface. Dataset for training and testing procedure was extracted by finite element model. According to the regression models, thickness of pad has the greatest influence on the non-uniformity. Moreover, elastic modulus and pad thickness are the optimal combination of non-uniformity. Results could be useful in the improvement of chemical polishing process.

Analysis of the Effects of Neuro-Fuzzy Control Configuration Parameters on PH Neutralization Process

Abstract The pH neutralization process is typical in chemical, biological and petrochemical industries. One of the major challenges to control it is to understand its nonlinearities and that requires several fine adjustments from conventional controls. Artificial Intelligence has been used to study these nonlinearities; one of them is Neuro-Fuzzy Logic, which was investigated in this work to develop controls dedicated to this process. These controls are formed by logical structures and may be adjusted to different configurations. In practical applications, it is highly important to adapt control parameters based on artificial intelligence to obtain better performance. The present work studied the effect of different configurations of a neuro-fuzzy control on the performance of a regulatory control to pH neutralization process by means of a virtual plant developed in both Indusoft© and Matlab© environments. For both variables, pH and reactor level control, membership function (MF) = [Gaussian], method “OR” = [probabilistic], method “E” = [product], type of MF output = [linear] and the optimization method = [hybrid], have improved control performance, which confirms the importance of configuration choices in neuro-fuzzy control adjustments. Moreover, the most determining factor in NFC performance is the types of membership functions.

An adaptive neuro-fuzzy logic based jamming detection system in WSN

Wireless sensor network (WSN) is employed in variety of applications ranging from agriculture to military. WSN is vulnerable to various security attacks, in which jamming attacks obstruct and disturb the exchange of information between sensor nodes in WSN by transmitting signals to jam legitimate transmission to cause a denial of service. Hence, it is essential to secure the sensor networks from jamming attacks. In this paper, two approaches: fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS)-based jamming detection system are proposed for detecting the presence of jamming by computing two jamming detection metrics, namely, packet delivery ratio and received signal strength indicator. FIS approach is based on Takagi–Sugeno fuzzy logic which optimizes the jamming detection metrics. ANFIS approach combines fuzzy logic and learning ability of the neural network to optimize the metrics for detecting various types of jamming. The proposed approaches are compared with existing system and themselves. The simulation result shows that the proposed ANFIS approach detects the jamming attacks as high as true detection ratio.

Combining DOE With Neurofuzzy Logic for Healthy Mineral Nutrition of Pistachio Rootstocks in vitro Culture.

The aim of this study was to determine the effects of Murashige and Skoog (MS) salts on optimal growth of two pistachio rootstocks, P. vera cv. “Ghazvini” and “UCB1” using design of experiments (DOE) and artificial intelligence (AI) tools. MS medium with 14 macro—and micro-elements was used as base point and its concentration varied from 0 to 5 × MS concentrations. Design of experiments (DOE) software was used to generate a five-dimensional design space by categorizing MS salts into five independent factors (NH4NO3, KNO3, mesos, micros and iron), reducing the experimental design space from 3,125 to just 29 treatments. Typical plant growth parameters such as shoot quality (SQ), proliferation rate (PR), shoot length (SL), and some physiological disorders including shoot-tip necrosis (STN) and callus formation at the base of explants (BC) were evaluated for each treatment. The results were successfully modeled using neurofuzzy logic software. The model delivered new insights, by different sets of “IF–THEN” rules, pinpointing the key role of some ion interactions ( × Cl−, K+ × × EDTA−, and Fe2+ × Cu2+ × ) for SQ, PR, and SL, whilst physiological disorders (STN and BC) were governed mainly by independent ions as Fe2+ and EDTA−, respectively. In our opinion, the methodology and results obtained in this study is extremely useful to understand the effect of mineral nutrients on pistachio in vitro culture, through discovering new complex interactions among macro—and micro-elements which can be implemented to design new media of plant tissue culture and improve healthy plant micropropagation for any plant species.

Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.

Application of adaptive neuro fuzzy inference system to support power transformer life estimation and asset management decision

Power transformer is a critical asset in electrical transmission and distribution networks that need to be carefully monitored during its entire operational life. Considering the fact that a significant number of worldwide in-service power transformers have approached the end of their expected operational life, utilities have adopted various transformer condition-based maintenance techniques to avoid any potential catastrophic failure to the equipment. The extent of transformer insulation system ageing can be quantified through measuring several diagnostic indicators such as interfacial tension number of the insulating oil which has a strong correlation with the number of transformer operating years. Moisture and furanic compounds generated due to paper insulation degradation are indicators for solid insulation ageing. This paper introduces a new adaptive neuro fuzzy logic model to estimate the life of mineral oil-filled power transformers based on the values of insulating oil interfacial tension number, furan content in oil and the moisture content within the cellulose insulation. Also, an integrated asset management decision model is proposed. Results of the proposed model are validated against practical data collected from utility and industry mineral oil-filled power transformers of different ratings, designs, operating conditions and lifespans.

Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling

Rigorous analysis of the determinants of volumetric mass transfer coefficient (kLa) and its accurate forecasting are of vital importance for effectively designing and operating stirred reactors. Majority of the available literature is limited to systems with low solids concentration, while there has always been a need to investigate the gas-liquid hydrodynamics in tanks handling high solid loadings. Several models have been proposed for predicting kLa values, but the application of neuro-fuzzy logic for modeling kLa based on combined operational and geometrical conditions is still unexplored. In this paper, an ANFIS (adaptive neuro-fuzzy inference system) model was designed to map three operational parameters (agitation speed (RPS), solid concentration, superficial gas velocity (cm/s)) and one geometrical parameter (number of curved blades) as input data, to kLa as output data. Excellent performance of ANFIS's model in predicting kLa values was demonstrated by various performance indicators with a correlation coefficient of 0.9941.

A design of intelligent QoS aware web service recommendation system

In web services environment, making an appropriate service recommendation to improve the web service composition ability and decrease composition cognition is a promising approach. Web service filtering is one of the essential technique for recommending QoS based web services. Collaborative Filtering (CF) plays major role in Web service recommendation which intends to predict missing QoS values of web services. QoS prediction techniques, emphasize more on user’s personalized influence of services and service QoS factors such as response time and throughput. The proposed model merges the location preference and personalized usage influences of the requester with Privacy, Demand, Satisfaction and Retention level assessment. Neuro Fuzzy Logic (NFL) is introduced to improve classification accuracy to intelligently identify the untrustworthy Web services. Intelligent Neuro Fuzzy Collaborative filtering (INFCF) for QoS aware web service recommendation is designed to enhance the QoS in web service recommendation.

ANFIS based Modelling of dewatering performance and polymer dose optimization in a wastewater treatment plant

Anaerobic digestion coupled with dewatering is a common and widely used sludge treatment methods in a wastewater treatment plant (WWTP). Many researchers have employed adaptive neuro-fuzzy logic inference system (ANFIS) successfully to optimize the output of anaerobic digesters. However, there is no published work on the application of ANFIS to optimize the performance of the dewatering section of a WWTP. In this study, ANFIS was applied to model the relationship between biosolid quality (total solid content in biosolid) to total solid, volatile solid content, volatile fatty acid of digested sludge and polymer dose. Historical data on various physico-chemical process parameters such as total solid, volatile solid, volatile fatty acid content of digested sludge, total solid content of biosolid and polymer dose from the dewatering section of a WWTP over a period of four years was used for the analysis. The modelling study showed that the optimum total solid content (DS TS) was in the lower DS TS range of 1.3–1.5% w/w and the optimum volatile solid content (DS VS) was also in the lower DS VS range of 70–75% of TS of the digested sludge. The Optimum polymer dose range of 20 to 60 kg/ton of dry solid (kg/TDS) showing better biosolid capture compared to higher polymer dose ranges for the same total solid and volatile solid content. It was found that lower total solid and volatile solid concentration coupled with relatively low polymer dose yield higher biosolid capture rate. ANFIS based predictive model are useful for determination of optimum polymer dose in efficient dewatering performance.

Intelligent Energy Management Strategy Based on Artificial Neural Fuzzy for Hybrid Vehicle

This paper proposes an intelligent energy management strategy for a hybrid hydraulic–electric vehicle in order to minimize its total energy consumption. It proposes first to model the vehicle total energy consumption and investigates the minimization of an expended energy function, formulated as the sum of electrical energy provided by the on-board batteries and consumed fuel. More precisely, it is proposed in this paper an intelligent controller that shows its capabilities of increasing the overall vehicle energy efficiency and, therefore, minimizing total energy consumption. The proposed strategy consists of an advanced supervisory controller at the highest level (third), which corresponds to a fuzzy system deciding the most appropriate operating mode of the system. In the second level, an intelligent optimal control strategy is developed based on neuro-fuzzy logic. Then, in the first level, there are local fuzzy controllers to regulate vehicle subsystems set points to reach the best operational performance. The advantage of the proposed strategy could be summarized as follows: first, it can be implemented online and second, reduces total energy consumption compared with several traditional methods. The proposed strategy validations are performed using a mix of automotive TruckMaker and MATLAB/Simulink developed software on several (standard or not) driving cycles.

A novel method for the production of core-shell microparticles by inverse gelation optimized with artificial intelligent tools

Numerous studies have been focused on hydrophobic compounds encapsulation as oils. In fact, oils can provide numerous health benefits as synergic ingredient combined with other hydrophobic active ingredients. However, stable microparticles for pharmaceutical purposes are difficult to achieve when commonly techniques are used. In this work, sunflower oil was encapsulated in calcium-alginate capsules by prilling technique in co-axial configuration. Core-shell beads were produced by inverse gelation directly at the nozzle using a w/o emulsion containing aqueous calcium chloride solution in sunflower oil pumped through the inner nozzle while an aqueous alginate solution, coming out from the annular nozzle, produced the beads shell. To optimize process parameters artificial intelligence tools were proposed to optimize the numerous prilling process variables. Homogeneous and spherical microcapsules with narrow size distribution and a thin alginate shell were obtained when the parameters as w/o constituents, polymer concentrations, flow rates and frequency of vibration were optimized by two commercial software, FormRules® and INForm®, which implement neurofuzzy logic and Artificial Neural Networks together with genetic algorithms, respectively. This technique constitutes an innovative approach for hydrophobic compounds microencapsulation.

Prediction of remaining useful life of cutting tools: a comparative study using soft computing methods

Predicting the remaining useful life of the partially degraded components and putting them to use will help to save natural resources to a great extent. This will reduce the overall cost, energy and protects environment. High productivity cutting tools used in manufacturing industry are generally expensive. As such, the accurate assessment of remaining useful life (for reuse) of any given tool is of great significance in any manufacturing industry. The main objective of this research is to develop a comprehensive methodology to assess the reuse potential of carbide-tipped tools. This paper presents the use of three soft computing methods, namely, artificial neural network, neuro fuzzy logic and support vector regression methods for the assessment of remaining useful life (RUL) of cutting tools. In this work, experiments are conducted based on Taguchi approach and tool life values are obtained. Tool life values are predicted using the aforesaid three soft computing methods and RUL obtained from these values are compared. It is found that the predictive neuro fuzzy method is capable of giving a better prediction of remaining useful tool life than the other methods.

Usability Estimation of Component Based Software System using Adaptive Neuro Fuzzy Approach

Cost effective development is the prime goal for software developers. To achieve this goal, nowadays, component-based software system (CBSS) are developed. In CBSS, the existing components are reused to develop a new software system which increases the reusability of components. It also reduces time and efforts of software developers, which is cost effective. The success or failure of software system depends on its usability. Usability can increase the market revenue. So, to increase the acceptance rate of CBSS among the users, it is important to evaluate the usability of CBSS before the software is released. In this paper, usability of CBSS is evaluated based on four input factors by using two widely used soft computing techniques, i.e., fuzzy logic and adaptive neuro fuzzy logic. Experimental results obtained from both the techniques are also compared and it is observed that ANFIS approach reduces the error rate and provide more accurate results. This research work will help the software developers to estimate the usability of CBSS in a more efficient manner.

Computer-Assisted Recovery of Threatened Plants: Keys for Breaking Seed Dormancy of Eryngium viviparum.

Many endangered plants such as Eryngium viviparum (Apiaceae) present a poor germination rate. This fact could be due to intrinsic and extrinsic seed variability influencing germination and dormancy of seeds. The objective of this study is to better understand the physiological mechanism of seed latency and, through artificial intelligence models, to determine the factors that stimulate germination rates of E. viviparum seeds. This description could be essential to prevent the disappearance of endangered plants. Germination in vitro was carried out under different dormancy breaking and incubation procedures. Percentages of germination, viability and E:S ratio were calculated and seeds were dissected at the end of each assay to describe embryo development. The database obtained was modeled using neurofuzzy logic technology. We have found that the most of Eryngium seeds (62.6%) were non-viable seeds (fully empty or without embryos). Excluding those, we have established the germination conditions to break seed dormancy that allow obtaining a real germination rate of 100%. Advantageously, the best conditions pointed out by neurofuzzy logic model for embryo growth were the combination of 1 mg L−1 GA3 (Gibberellic Acid) and high incubation temperature and for germination the combination of long incubation and short warm stratification periods. Our results suggest that E. viviparum seeds present morphophysiological dormancy, which reduce the rate of germination. The knowledge provided by the neurofuzzy logic model makes possible not just break the physiological component of dormancy, but stimulate the embryo development increasing the rate of germination. Undoubtedly, the strategy developed in this work can be useful to recover other endangered plants by improving their germination rate and uniformity favoring their ex vitro conservation.

Artificial Neuro-Fuzzy Logic Based Non-Sinusoidal Synchronous Reluctance Motor for Torque Ripple Minimization

Artificial Neuro-Fuzzy Logic Based Non-Sinusoidal Synchronous Reluctance Motor for Torque Ripple Minimization

Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm

In the present work, autonomous mobile robot (AMR) system is intended with basic behaviour, one is obstacle avoidance and the other is target seeking in various environments. The AMR is navigated using fuzzy logic, neural network and adaptive neuro-fuzzy inference system (ANFIS) controller with safe boundary algorithm. In this method of target seeking behaviour, the obstacle avoidance at every instant improves the performance of robot in navigation approach. The inputs to the controller are the signals from various sensors fixed at front face, left and right face of the AMR. The output signal from controller regulates the angular velocity of both front power wheels of the AMR. The shortest path is identified using fuzzy, neural network and ANFIS techniques with integrated safe boundary algorithm and the predicted results are validated with experimentation. The experimental result has proven that ANFIS with safe boundary algorithm yields better performance in navigation, in particular with curved/irregular obstacles.

Predicting optimal in vitro culture medium for Pistacia vera micropropagation using neural networks models

In this study, artificial intelligence techniques—specifically artificial neural networks (ANNs) in combination with fuzzy logic (neurofuzzy logic) or with genetic algorithms (ANNs–GA)—have been employed, as modeling tools, to get insight, to predict and to optimize the effect of several independent factors on four growth parameters during Pistacia vera micropropagation. Twenty-six media ingredients, including mineral ions (or salts), glycine, vitamins and plant growth regulators (PGRs) at different concentrations, were used as inputs and four growth parameters: proliferation rate, shoot length, total and healthy fresh weight as outputs on the models. The IF-THEN rules from neurofuzzy logic models have allowed discovering the positive (BAP, nicotinic-acid and pyridoxine-HCl) and negative (NO3 −, Mg2+, Ag+ and gluconate−) effects on the growth parameters and the fundamental role of BAP over all of them. Also, ANNs–GA technology has permitted to estimate the best combination of media ingredients to simultaneously maximize the four parameters of growth: 4.4 new shoots per explant; 28.7 mm length; 1.1 and 0.53 g total and healthy fresh weight, respectively, minimizing physiological disorders. In our opinion, the information obtained in this study is extremely useful to improve the massive multiplication of pistachio plant, in particular, but also demonstrate the ability of artificial intelligence technology to design plant tissue culture media with predictable and tailorable characteristics.

Cluster based Routing in Sensor Network using Soft Computing Techniquies: A Survey

Research in Wireless Sensor Networks (WSNs) has gained importance in the recent years. These networks are battery driven and are usually deployed in hazardous or isolated areas. The main objective of these networks is to collect environmental data and forward it to the base station. Hence routing has become an important area of research. Energy efficiency and timely delivery of sensed data are critical criteria for these networks. The position of sensor nodes should not be pre-determined. Clustering is a key method used to extend the lifetime of a sensor network. It has many applications, such as building supervision, industrial quality management, and environmental monitoring. The characteristics of WSNs are Low energy usage, Dynamic network operations and autonomous process of networks. Soft computing techniques can be incorporated to achieve better performance. This paper surveys the recent trends in routing contain fuzzy logic and Neuro fuzzy logic based clustering techniques and presents a comparative study of the various approaches.

Artificial Intelligence Tools for Scaling Up of High Shear Wet Granulation Process

The results presented in this article demonstrate the potential of artificial intelligence tools for predicting the endpoint of the granulation process in high-speed mixer granulators of different scales from 25L to 600L. The combination of neurofuzzy logic and gene expression programing technologies allowed the modeling of the impeller power as a function of operation conditions and wet granule properties, establishing the critical variables that affect the response and obtaining a unique experimental polynomial equation (transparent model) of high predictability (R2 > 86.78%) for all size equipment. Gene expression programing allowed the modeling of the granulation process for granulators of similar and dissimilar geometries and can be improved by implementing additional characteristics of the process, as composition variables or operation parameters (e.g., batch size, chopper speed). The principles and the methodology proposed here can be applied to understand and control manufacturing process, using any other granulation equipment, including continuous granulation processes.