Hierarchical Fuzzy Inference System Research Articles

Комплексная оценка экологической, социальной и управленческой эффективности предприятий рыбодобывающей и рыбоперерабатывающей отрасли

The purpose of the work is to form provisions for determining a comprehensive assessment of the effectiveness of the activities of a fishing and fish processing company in the field of ecology, social responsibility and in matters of management activities. When forming this assessment, indicators are taken into account that characterize the effectiveness of environmental protection measures; improvement of working conditions of employees, organization of corporate governance. To aggregate these characteristics, methods based on the position of the theory of fuzzy sets are proposed. The computing complex itself for processing information about the state of controlled parameters is implemented in the form of a hierarchical fuzzy inference system and an additional block that is used to distribute objects of analysis by state classes. The computing complex includes fuzzy inference systems based on the Sugeno algorithm. As a result of aggregation of the estimated parameters, two estimates are formed: one allows you to rank the analyzed objects, the second – to correlate the object of analysis with a certain class of state. The simulation showed that the computing complex has a high sensitivity: if at least one of the input variables is in the range of low (bad) values, this leads to a decrease in the value of the final score. This property makes it possible to exclude the occurrence of massaging of “bad” values of individual variables with “good” values of other variables. The results of the study confirmed the ability of the complex to distribute the objects of analysis by state classes. The results can be used for the development of corporate governance systems that are oriented for use in enterprises of the fisheries industry.

Expert knowledge-based modelling approach for mapping beekeeping suitability area

It is becoming increasingly accepted that beekeeping is declining due to the damaging effect of global changes such as climate and land-use change that directly and indirectly impact Apis Melliferas. Despite numerous investigations, a comprehensive study that incorporates both global and local knowledge has yet to be conducted. For a long time, researchers have suggested that expert knowledge should be taken into account when creating decision support tools for managing activities related to natural resources, such as beekeeping. Unlike previous studies, this research seeks to tackle these questions while also introducing the concept of ecosystem service in modelling, offering a fresh perspective on sustainable land use. To achieve this goal, we combined several methods, including using literature knowledge, beekeeper knowledge, and multi-source geospatial data. These data are employed in a hierarchical fuzzy inference system in a unified way. The proposed approach was applied in the Québec region and the suggested technique appears to be both reliable and effective. The validation step revealed that the landscape variable, particularly the area used for agriculture or grassland, had the greatest impact on changes in hive weight throughout the season. In addition, we demonstrated that meteorological factors such as rainfall and relative humidity are strongly correlated to beekeeping. We showed that access to data and knowledge can be a critical factor in decision-making in the beekeeping industry, and thus we suggest that wild-bees conservationists, decision-makers, farmers, beekeepers, and other stakeholders adopt a collaborative approach.

Adaptive Dynamic Path Planning Method for Autonomous Vehicle Under Various Road Friction and Speeds

Path planning is a crucial technology for autonomous vehicle (AV). However, it is difficult to adapt to dynamic driving environment, and AV may lose lateral dynamic stability due to high speed and various friction. This paper presents an adaptive dynamic path planning method (ADPPM) for AV to address the challenges. The ADPPM is comprised of three components: 1) A dynamic state-fused steering decision method based on a hierarchical fuzzy inference system is designed to calculate the steering position of AV in each iteration step, and the method fuses the multi-state of the dynamic environment; 2) dynamic path optimization method is designed to reduce the mean curvature of the path based on the particle swarm optimization method, which improves the lateral dynamics stability of AV; 3) adaptive speed inference method is proposed to provide desired steering speed for AV according to various road friction and reduce AV’s steering burden. The ADPPM provides path planning in the dynamic environment, and it also improves the stability of AV under various road friction and speeds. Finally, the proposed method is verified by CarSim.

A Serious Game System for Upper Limb Motor Function Assessment of Hemiparetic Stroke Patients.

Stroke often results in hemiparesis, impairing the patient's motor abilities and leading to upper extremity motor deficits that require long-term training and assessment. However, existing methods for assessing patients' motor function rely on clinical scales that require experienced physicians to guide patients through target tasks during the assessment process. This process is not only time-consuming and labor-intensive, but the complex assessment process is also uncomfortable for patients and has significant limitations. For this reason, we propose a serious game that automatically assesses the degree of upper limb motor impairment in stroke patients. Specifically, we divide this serious game into a preparation stage and a competition stage. In each stage, we construct motor features based on clinical a priori knowledge to reflect the ability indicators of the patient's upper limbs. These features all correlated significantly with the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), which assesses motor impairment in stroke patients. In addition, we design membership functions and fuzzy rules for motor features in combination with the opinions of rehabilitation therapists to construct a hierarchical fuzzy inference system to assess the motor function of upper limbs in stroke patients. In this study, we recruited a total of 24 patients with varying degrees of stroke and 8 healthy controls to participate in the Serious Game System test. The results show that our Serious Game System was able to effectively differentiate between controls, severe, moderate, and mild hemiparesis with an average accuracy of 93.5%.

Context-Aware Multi-Criteria Handover at the Software Defined Network Edge for Service Differentiation in Next Generation Wireless Networks

The densified deployment of heterogeneous networks coexisting with a variety of overlapping cells has emerged as a viable solution for next generation wireless networks. Despite numerous advantages, the heterogeneity and denseness also raise complicated handover management issue. Nonetheless, most existing handover methods generally depend on one or more objective attributes, and rarely consider the subjective demands of personalized users and specific applications that demand differentiated services. Through decomposing the control plane and data plane, software defined network(SDN) offers a flexible architectural paradigm to overcome these challenges. In this article, we first develop an SDN-driven handover architecture that is capable of perceiving global network status and requirements from various perspectives, including the physical layer, users, and applications. Then, a context-aware multi-criteria handover mechanism is developed in the SDN edge to provide differentiated services. Considering the numerous complicated factors, the handover decision is made based on a hierarchical fuzzy inference system to process diverse attributes and vague requirements described in natural language. Finally, we evaluate the performance of our proposed scheme through a combination of extensive simulations and real-world experiments. The results demonstrate that our solution outperforms the baseline handover schemes, more efficiently providing differentiated services with respect to throughput, bandwidth cost, and application satisfaction, and is efficient and feasible in practice.

A novel hierarchical fuzzy inference system for supplier selection and performance improvement in the oil & gas industry

ABSTRACT Evaluation of suppliers is essential to increasing competitive power, customer satisfaction, and profitability. Oil and gas companies can use this research to evaluate suppliers and map the potential path forward for future collaborations. Six supply chain managers in Iran designed HFIS for the oil and gas industry. Shannon Entropy was used to determine the relative weights of suppliers concerning overall uncertainty because the Oil and Gas industry uses many unstructured Key Performance Indicators (KPIs). Using Matlab Toolbox FIS, a future cooperation roadmap was developed. Experts suggested future collaboration with certain suppliers based on the HFIS results. The future cooperation strategy proposed by the framework is highly in line with their expectations. FIS results indicate that the proposed can help select the most appropriate suppliers for cooperation while providing a roadmap for weaker suppliers to improve their performance.

ВЫЧИСЛИТЕЛЬНЫЙ КОМПЛЕКС ДЛЯ ОЦЕНКИ СОЦИАЛЬНО-ЭКОНОМИЧЕСКОЙ ЭФФЕКТИВНОСТИ ПРЕДПРИЯТИЙ РЫБОДОБЫВАЮЩЕЙ ОТРАСЛИ

The paper proposes theoretical foundations for assessing the integrated efficiency of fishing industry enterprises. A set of parameters is proposed, including factors that characterize the environmental and social efficiency of the enterprise, as well as factors affecting the effectiveness of the enterprise management system. The factors for assessing environmental efficiency included assessments characterizing emissions of greenhouse gases and other harmful substances, the level of energy consumption, as well as the harm caused to water resources. The factors for assessing social effectiveness included assessments related to the level of wages, the level of injuries, the assessment of the effectiveness of labor protection measures and the assessment of staff housing conditions. To determine the effectiveness of corporate governance, factors related to the assessment of the quality management system, the assessment of the quality of risk management systems, and the assessment of the overall financial condition of the enterprise are taken into account. The proposed computer complex consists of two modules. The first module is used to form an integral estimate. The second module is used to correlate the received assessment to one of the seven classes of the state. The proposed provisions are based on the methods of fuzzy set theory and the property of the hierarchical fuzzy inference system, which is based on the Sugeno algorithm. The results of the study led to the conclusion that the system allows you to track the sharp deviation of each of the parameters. The presence of a block of grouping values makes it possible to form classes of states of the evaluated objects. Each of the state classes is identified with one of the seven levels of the ESG score value. The conclusions made open up the possibility of further development of corporate information systems.

Investigation of Hierarchical Fuzzy Inference Systems, when Obtaining Integral Estimates of the Analyzed Objects

The purpose of this paper is to study the patterns of the formation of output values in hierarchical systems offuzzy inference. Hierarchical fuzzy inference systems (HFIS) are used to aggregate heterogeneous parameters during the assessment of the state of various elements of complex systems. The use of HFIS allows avoiding the "curse" of the dimension associated with a strong increase in the number and complication of the structure of the production rule, which is characteristic of conventional fuzzy inference systems (FIS), which aggregate the results of interaction of different values of input variables in one knowledge base. As part of the research, numerical experiments were carried out to study the features of the formation of output patterns in HFIS, based on FIS using the Mamdani and Takagi-Sugeno algorithms. As a result of the experiment, it was shown that the output values of the studied HFIS tend to be grouped in the region of fixed values, and the output pattern itself acquires a stepwise character. The revealed property allows using HFIS to distribute the objects of the analyzed sample into groups of states. This property can be used to solve problems of distributing objects into groups in conditions when it is difficult to form a training sample for machine learning methods, but at the same time there is knowledge of the expert group about the features of the functioning of the object of research. Additionally, the paper investigates the features of the formation of output patterns depending on the parameters of the membership functions describing the input variables in HFIS, which are based on FIS using the Mamdani algorithm and HFIS, which are based on FIS using the Takagi-Sugeno algorithm.

A comparative analysis on groundwater vulnerability models-fuzzy DRASTIC and fuzzy DRASTIC-L.

Groundwater vulnerability assessment using the fuzzy logic technique is attempted in this study. A hierarchical fuzzy inference system is created to serve the selected objective. The parameters considered in this study are similar to the seven parameters used in conventional DRASTIC methods; however, the effect of land use and land cover is studied by including it as an additional parameter in a model. A hierarchy is created by comparing two input parameters, say (D and R), and the output of the same is paired as an input with the third parameter (A) and so on using the fuzzy toolbox in MATLAB. Thus, the final output of fuzzy inference systems six and seven (FI6 and FI7) is defuzzified and mapped using ArcGIS to obtain the groundwater vulnerability zones by fuzzy DRASTIC and fuzzy DRASTIC-L. Each map is grouped into five vulnerability classes: very high, high, moderate, low, and very low. Further, the results were validated using the observed nitrate concentration from 51 groundwater sampling points. The receiver operating curve (ROC) technique is adopted to determine the best suitable model for the selected study. From this, area under the curve is estimated and found to be 0.83 for fuzzy DRASTIC and 0.90 for fuzzy DRASTIC-L; the study concludes that fuzzy DRASTIC-L has a better value of AUC suits best for assessing the groundwater vulnerability in Thoothukudi District.

Fuzzy inference systems based on multi-type features fusion for intra-hour solar irradiance forecasts

The cloud cover information in ground-based cloud (GBC) images is important to direct normal irradiance (DNI) prediction. In order to obtain better performance, the DNI forecasting model needs to incorporate the features from GBC images and numerical time series. In this paper, to resolve the issues of feature fusion with various types, three novel fuzzy inference systems (FIS) models are proposed for intra-hour DNI prediction. The features of the GBC image and the numerical time series are firstly fuzzified by clustering and grid partition, respectively. Then, the hybrid fuzzification and optimization method lead to the presented two hierarchical fuzzy inference systems (HFIS) models and an adaptive neuro-fuzzy inference system (ANFIS) model for DNI prediction. The performance of the proposed models is validated with the data from the National Renewable Energy Laboratory (NREL) from January 1, 2018 to December 31, 2018. Experiments demonstrate that the proposed models outperform the reference model, and the best model is capable of achieving 19.66% improvement over the reference model for 10-minute ahead DNI prediction.

An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks

The security of energy supplies requires the depletion of potential fault events in power transmission networks. To achieve this, sufficient lead time before the happening of a fault event is indispensable for preparing countermeasures. With this inspiration, this paper establishes the fuzzy inference with rare association rule learning system. This ensemble system is designed for the long-term prediction of the spatiotemporal distribution of such energy security weaknesses, that is, to predict when and where these events are more expected to appear. In this system, merely the environmental features rather than the electrical features are needed as inputs. All the selected input features are divided into discrete and continuous features, and are evaluated separately. For the discrete features, the rare association rule learning model is implemented so that the rarely distributed environmental elements are extracted and diagnosed specifically. The risk indices of each element on the overall reliability are worked out as well. For the continuous features, a hierarchical fuzzy inference system along with the rare association rule learning model is deployed to calculate the corresponding risk indices of all the elements. In the hierarchical fuzzy inference system, the probabilistic fuzzy risks are employed instead of the direct fuzzy risks. Then the relative weights of these two sides are optimized. At last, an empirical case based on a practical transmission network is conducted, and the flexibility and the robustness of the proposed system during real applications can be validated consequently.

A fuzzy inference approach to analysis of LPV-200 procedures influence on air traffic safety

Introducing new technical and organizational solutions in air transport requires demonstrating that the level of safety will not be reduced. The LPV-200 (Localizer Performance with Vertical Guidance) approach for landing procedures represent a great opportunity for development of small, poorly equipped airports, as they permit precise landing without costly investment in the ILS (Instrument Landing System). The aim of this paper is to assess the effects of the introduction of LPV-200 procedures for air traffic safety which was determined by the probability of a CFIT (Controlled Flight Into Terrain) accident – PoC. Factors affecting PoC are of a diverse nature, some of which are subjective and cannot be expressed precisely. Therefore, PoC assessment uses fuzzy logic methods, and more specifically hierarchical fuzzy inference systems, with a knowledge base obtained from experts. As a result of simulation experiments, PoC was determined for airports with various levels of navigational equipment. It was also found that the introduction of LPV-200 procedures allows the reduction of PoC, with the highest effect being achieved for the least equipped airports. Also, in the event of failure of the main approach assistance system ILS, the use of LPV-200 procedures allows maintaining PoC at the same or close to the basic value level. The results of our research indicate that the introduction of LPV-200 procedures is clearly positive for the commercial use of small, less equipped aerodromes. We have shown that thanks to employing LPV-200 procedures it is possible to keep the PoC at a level similar to typical commercial airports.

Correction to: On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

Correction to: On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

On the use of hierarchical fuzzy inference systems (HFIS) in expert-based landslide susceptibility mapping: the central part of the Rif Mountains (Morocco)

The main purpose of this study is to investigate the use of hierarchical fuzzy inference systems (HFISs) in expert-based landslide susceptibility mapping in a data-scarce region. Taounate-Ain Aicha and Tahar Souk regions in the central part of the Rif Mountains in Morocco were selected as the case study area. The research was performed in three main stages: (i) the landslide inventory of the region was produced and the conditioning factors were evaluated; (ii) the theoretical background for HFIS was introduced; and (iii) different types of structures and methods of HFIS were investigated in the construction of expert-based models; the landslide information was only used for validation of the expert-based models in this stage. Regarding the inference methods, the defuzzification-free hierarchical fuzzy system (DF-HFS) has not only the remarkable advantage of low cost of computation but also preservation of information. The most successful result was acquired from the model developed using HFISs, which was designed by considering a defuzzification-free hybrid structure with standard membership functions. This research is the first study in which HFISs are evaluated in expert-based landslide susceptibility mapping. HFISs and the rule-generation algorithm implemented in this study will allow fuzzy systems to be applied effectively not only in landslide susceptibility mapping but also in other geoscientific and geo-engineering solutions.

Water Supply Pipeline Risk Index Assessment Based on Cohesive Hierarchical Fuzzy Inference System

As populations grow, facilities such as roads, bridges, railways lines, commercial and residential buildings, etc., must be expanded and maintained. There are extensive networks of underground facilities to fulfil the demand, such as water supply pipelines, sewage pipelines, metro structures, etc. Hence, a method to regularly assesses the risk of the underground facility failures is needed to decrease the chance of accidental loss of service or accidents that endanger people and facilities. In the proposed work, a cohesive hierarchical fuzzy inference system (CHFIS) was developed. A novel method is proposed for membership function (MF) determination called the heuristic based membership functions determination (HBMFD) method to determine an appropriate MF set for each fuzzy logic method in CHFIS. The proposed model was developed to decrease the number of rules for the full structure fuzzy inference system with all rule implementation. Four very crucial parameters were considered in the proposed work that are inputs to the proposed CHFIS model in order to calculate the risk of water supply pipelines. In order to fully implement the proposed CHFIS just 85 rules are needed while using the traditional Mamdani fuzzy inference system, 900 rules are required. The novel method greatly reduces implementation time and rule design sets that are extremely time consuming to develop and difficult to maintain.

Cognitive and Hierarchical Fuzzy Inference System for Generating Next Release Planning in SaaS Applications

Cognitive and Hierarchical Fuzzy Inference System for Generating Next Release Planning in SaaS Applications

Fuzzy signaling game of deception between ant-inspired deceptive robots with interactive learning

In this study the robotic deception phenomenon is raised in the framework of a signaling game which utilizes fuzzy logic and game theory along with inspirations from nature. Accomplishing the fuzzy signaling strategy set for deceptive players serves as a great part of our contribution and on this aim, hierarchical fuzzy inference systems support receiver’s actions and sender’s ant-inspired deceptive signals (track and pheromone). In addition, special deceptive robots and visually-supported experimental environment are also provided. The fuzzy behavior of robots defines the strategy type of players. The final result of deception process depends on this strategy type which leads to proposing a payoff matrix in which each cell of mutual costs is defined with special supporting logic related to our deception game with pursuit–evasion applications. Furthermore, motivated by animal signaling, through applying mixed strategies on deceiver’s honesty level and rival’s trust level, the corresponding learning dynamics are investigated and the conceptual discussion put forward serves as a proof to the smart human-like behavior that occurs between the robots: the interactive learning. Simulation results show that robots are capable of interactive learning within deceptive interaction and finally change their strategies to adopt themselves to new situation occurred due to opponent’s strategy change. Because of repetitive change in strategies as a result of learning, the conditions of a persistent deception without breakdown holds for this game where deceiver can frequently benefit from deception without leaving rival to lose its trust totally. The change in strategy will happen after a short time needed to learn the new situation. In rival’s learning process, this short time, which we call the ignorance time, exactly is the period that deceiver can benefit from deception while its evil intends are still concealed. Moreover, in this study an algorithm is given for the proposed signaling game of deception and an illustrative experiment in the introduced experimental environment demonstrates the process of a successful deception. The paper also gives solution to the proposed game by analyzing mixed Nash equilibrium which turns out to be the interior center fixed point of the learning dynamics.

An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data.

An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.

Detailed Sponge City Planning Based on Hierarchical Fuzzy Decision-Making: A Case Study on Yangchen Lake

We proposed a Hierarchical Fuzzy Inference System (HFIS) framework to offer better decision supports with fewer user-defined data (uncertainty). The framework consists two parts: a fuzzified Geographic Information System (GIS) and a HFIS system. The former provides comprehensive information on the criterion unit and the latter helps in making more robust decisions. The HFIS and the traditional Multi-Criteria Decision Making (MCDM) method were applied to a case study and compared. The fuzzified GIS maps maintained a majority of the dominant characteristics of the criterion unit but also revealed some non-significant information according to the surrounding environment. The urban planning map generated by the two methods shares similar strategy choices (6% difference), while the spatial distribution of strategies shares 69.7% in common. The HFIS required fewer subjective decisions than the MCDM (34 user-defined decision rules vs. 141 manual evaluations).

A novel spatial index using spatial analyses and hierarchical fuzzy expert system for obtaining green TOD: a case study in Tehran city

Sustainable development is a vital and challenging factor for managing urban growth smartly. This factor contains three main components, namely economic growth, ecological protection and social justice. Green Transit-Oriented Development (GTOD) is a consummate planning approach in line with those components. Implementation of GTOD in an urban area is underpinned by its quantification. Therefore, a quantitative spatial index based on several indicators related to TOD and Green urbanism concepts should be developed. In this study, Geo-spatial Information Science and hierarchical fuzzy inference system (HFIS) were employed to calculate the indicators and aggregate them, respectively. In order to showcase the feasibility of the proposed method, it was implemented in a case study area in the City of Tehran, Iran. The result of this method is an integrated spatial GTOD index, which measures the neighbourhoods’ GTOD levels. These measurements specify weaknesses and strengths of neighbourhoods’ factors. Therefore, this index helps decision-makers to plan neighbourhoods based on land use and public transit views. Additionally, the HFIS method helps decision-makers to consider criteria and indicators with their inherent uncertainties and aggregate them with much fewer rules. For evaluating the results, the developed GTOD index was assessed with municipal action planning and attraction maps. According to the outcomes of the assessment, it is concluded that the proposed method is adequately robust and efficient for smart and sustainable urban planning.