Hybrid Neural Fuzzy System Research Articles

A Hybrid Fuzzy-Neural Model for Pattern Detection to Predict the Egyptian Stocks Price Movement Direction

In this paper, a hybrid fuzzy-neural system for Egyptian stocks price prediction is proposed. The model helps choosing the right stock mixture with the highest profit within a certain risk factor. A hybrid fuzzy-neural system is applied to significantly save effort and time of portfolio managers. The model increases the individual investors’ local market understanding by providing buy and sells signals that reflect market sentiments, breaking news and technical analysis expectations. An implemented system of the proposed model has demonstrated a promising performance of the applied test datasets containing 100 Stock Symbols over the past 9 years (January 2009-July 2018). The prediction accuracy of the model is computed by comparing the applied system predicted results against the actual results of the Egyptian stock market during the test period.

Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology

Adaptive neuro-fuzzy inference system (ANFIS) has emerged as a synergic hybrid intelligent system. It combines the human-like reasoning style of fuzzy logic system (FLS) with the learning and computational capabilities of artificial neural networks (ANNs). ANFIS has several applications related to food processing and technology. The first part of this review provides a brief overview and discussion of ANFIS including: the general structure and topology, computational considerations, model development and testing. In the second part, two detailed examples are explained to demonstrate the capabilities of ANFIS in comparison with other modeling methods, followed by a brief but comprehensive discussion of ANFIS applications in different food processing and technology areas. The applications are divided into five main categories: food drying, prediction of food properties, microbial growth and thermal process modeling, applications in food quality control and food rheology. In all applications, the performance of ANFIS is compared to other methods such as ANNs, FLS and multiple regressions when available. It is concluded that, in most applications, ANFIS outperforms other modeling tools such as ANNs, FIS or multiple linear regression. Finally, some application guidelines, advantages and disadvantages of ANFIS are discussed.

Fuzzy System Design for Security and Environment Control Applications

As the amount of data available to researchers continues to grow, sharp class distinctions generated through Boolean logic fundamentally limit researchers' abilities to adequately process data into useable information. The application of fuzzy systems to real world problems allows us to synthesize this raw data into meaningful results. This paper demonstrates how fuzzy theory can be applied to real life application's design where rules can be generated explicitly through an expert system, or implicitly derived through a hybrid neural-fuzzy system. The paper illustrates the success of the fuzzy system use in applications by providing examples in the context of mobile device security evaluation and air quality evaluation and prediction. The examples represent important application areas within engineering and information technology domains.

Health products sales forecasting using computational intelligence and adaptive neuro fuzzy inference systems

In our days the importance of reducing the inventory level in a healthcare organization is increasing fast. As a result, the value of an accurate supply forecast is becoming more relevant. The main objective of this paper is to analyze and compare some of the most popular and widely applied techniques available based on computational intelligence. In addition, it aims to demonstrate the competitiveness of the aforementioned using real-world data. The methods that are employed include neural networks (feed forward, radial basis, generalized regression, and recurrent networks) and the hybrid neural fuzzy system (ANFIS). The experimental part of this study is conducted with the use of sales’ data extracted from the database of a major Greek medical supplier. A two-year period was employed in order to gather the appropriate sales figures about some of the most popular medicines and subsequently each technique’s forecasting ability was tested against a third year. The parameters of each technique are then fine-tuned in order to minimize the performance functions. Finally, a brief statistical analysis of the techniques used is performed to facilitate the comparison between them and define the most appropriate method for this particular issue.

A Hybrid Fuzzy-Neural System for Computer-Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features

The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.

A fuzzy–neural hybrid system of simulating typhoon waves

This study addresses a fuzzy–neural hybrid system of simulating typhoon waves. A membership function based on the fuzzy theory is expressed by a union Gaussian function to illustrate the rapid wave decaying. Four areas separated by two lines which intersect at the Hua-Lien harbor indicate the case of typhoon's position and propagation. Better simulation performance of the peak wave heights and their occurrence time in both the learning stage and the verification stage simulated by the NF2 model than by the NF1 model is identified. The wave decaying due to land effect is well described by the NF2 model. The NF2 model is applicable for well simulating typhoon waves during the whole period of a typhoon approaching to Taiwan.

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid position from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The AFLC algorithm is applied to the Anderson iris data and laser-luminescent finger image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets.