Multilayer Fuzzy Inference System Research Articles

Application of fuzzy analytic hierarchy process – multi-layer fuzzy inference system in product design evaluation

To enhance profitability of production cycle, any manufacturer needs effective product design and evaluation procedures. This study proposed a novel approach combining fuzzy analytic hierarchy process (FAHP) and multi-layer fuzzy inference system (MFIS). It is based on consumer online comments to improve product design. This method possesses several advantages over traditional design evaluation methods. It can quickly acquire consumer preferences, effectively handle multi-criteria decision problems and integrate uncertain and fuzzy information. The Fuzzy Analytic Hierarchy Process–Multi-layer Fuzzy Inference System (FAHP-MFIS) involves the following steps: screening of factors, hierarchical modeling, quantification of qualitative factors, and conversion of these factors into quantitative values. It is a knowledge-based system that uses logical rules. The quantity and levels of input variables directly correlates with the quantity of logical rules. However, with multi-factor and multi-level inference, the establishment of a rule base becomes impractical due to the overwhelming number of rules. To address this issue, the Taguchi orthogonal table is applied to reduce the number of logical rules. Taking a household oxygen generator for medical devices as an example, the proposed model is applied in real-time. In the first stage, web crawlers are used to collect user reviews of the household oxygen generators on large e-commerce platforms. Latent Dirichlet Allocation (LDA) models are used to screen for principal and sub-factors in the second stage. Then, sub-factors of the FAHP screening are used as inputs, and the principal factors are used as outputs. In the third stage, priority indicators are established based on principal factors such as Appearance, Basic Function, and Advanced Function. Established evaluation models are then used to rank the selected designs. The results show that the higher the priority index value of the product design scheme, the better the scheme, and vice versa. This study holds significant reference value in aiding enterprises to enhance the efficiency of their manufacturing cycle and determining the direction of product design and innovation with improved pace and accuracy. Moreover, it can be applied to other fields such as supply chain management, risk assessment, and investment decisions.

Hybrid Fuzzy Neural Search Retrieval System

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.

Evaluation of groundwater vulnerability to nitrate in shallow aquifer using multi-layer fuzzy inference system within GIS environment

Groundwater aquifer vulnerability to nitrate has become a major concern to all stakeholders involved in managing the quality of water in relation to human health. This paper presents fuzzy inference system (FIS) as an alternative to the conventional overlay index methods for the evaluation of groundwater vulnerability to nitrate pollution in watershed scale within GIS environment. A multilayer FIS model is developed taking advantage of expert knowledge-based DRASTIC system in order to overcome the problem of rule explosion. A case study relating to groundwater vulnerability assessment in shallow aquifer of Kathmandu basin in Nepalis accomplished. The correlations between groundwater vulnerability indices and nitrate-N concentration values were carried out for the validation of vulnerability models. The result shows that the best combination is obtained from fuzzy vulnerability model (r = 0.52) followed by DRASTIC method (r = 0.38). We have also developed a groundwater nitrate-N risk map by integrating groundwater aquifer vulnerability, the nitrate-N distribution map (pollution severity) and the probability of occurrence map based on the limiting threshold value of nitrate-N. Spatial distribution map of nitrate-N shows that nearly 10% of the study area lies within high hazard class which exceeds limiting value of 10 mg/L nitrate-N value, and nearly 50% of the shallow aquifer fall within medium hazard class which has impacted level of nitrate-N, i.e., between 2 mg/L and 10 mg/L. These results also indicate that the northern part of the Kathmandu basin and highly permeable alluvial deposits are dominated by very high vulnerability level, and are also under a threat of high nitrate-N risk. Groundwater aquifer vulnerability and nitrate risk maps are expected to facilitate design and development strategies towards protecting environment and preventing further deterioration of the affected aquifers.

Development of multilayer fuzzy inference system for diagnosis of renal cancer

In this research work, a new multilayer fuzzy inference system is proposed for diagnosis of renal cancer. This proposed automated diagnosis of renal cancer using multilayer Mamdani fuzzy inference system can help to classify the different stages of r