We chronicle the research on the fusion technology of neural networks and fuzzy systems (NN+FS), the models that have been proposed from this research, and the commercial products and industrial systems that have adopted these models. First, we review the NN+FS research activity during the early stages in Japan, the US, and Europe. Next, following the classification of NN+FS models, we show the...

The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) l...

Rotor Flux based Model Reference Adaptive System (RF-MRAS) is the most popularly used conventional speed estimation scheme for sensor-less IM drives. In this scheme, the voltage model equations are used for the reference model. This encounters major drawbacks at low frequencies/speed which leads to the poor performance of RF-MRAS. Replacing the reference model using Neural Network (NN) based fl...

Recommender systems are now widely used in e-commerce applications to assist customers to find relevant products from the many that are frequently available. Collaborative filtering (CF) is a key component of many of these systems, in which recommendations are made to users based on the opinions of similar users in a system. This paper presents a model-based approach to CF by using supervised A...

One of the advantages of GPS/INS integration is that the integrated solution can provide continuous navigation capability even during GPS outages. However, bridging the GPS outages is still a challenge when MEMS inertial sensors are used. Methods being currently explored by the research community include applying vehicle dynamics constraints, optimal smoother, and neural network (NN) algorithms...

A quasi-linear ARX neural network model (QARXNN) is a nonlinear model built using neural networks (NN). It has a linear-ARX structure where NN is an embedded system to give the parameters for the regression vector. There are two contributions in this paper, 1) Hierarchical Algorithms is proposed for the training of QARXNN model, 2) an adaptive learning is implemented to update learning rate in ...

The novel Hessian-based pruning (HBP) technique to optimize the feed-forward (FF) neural network (NN) configuration in a dynamic manner is proposed. It is then used to optimize the extant NNC (Neural Network Controller) as the verification exercise. The NNC is designed for dynamic buffer tuning to eliminate buffer overflow at the user/server level. The HBP optimization process is also dynamic a...

Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function. Recent research shows that embedding T1FLS on an NN or embedding IT2FLS on an NN can be very effective for a wide number of non-linear complex systems, especially when handling imperfect information. In this paper we...

Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) archit...

The complexity of a Neural Network (NN) architecture has long been identified as of crucial importance for the NN overall generalization capability: a network which is too simple or too complex will generalize poorly, while having performances on learning set poor for the former, or close-to-perfect for the latter. For applications in image processing, the problem is usually particularly accute...