A K -means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

Abstract(#br)This paper proposes a new medical diagnosis algorithm that uses a K -means interval type-2 fuzzy neural network (KIT2FNN). This KIT2FNN classifier uses a K -means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the K -means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient..

A recurrent emotional CMAC neural network controller for vision-based mobile robots

Vision-based mobile robots often suffer from the difficulties of high nonlinear dynamics and precise positioning requirements, which leads to the development demand of more powerful nonlinear approximation in controlling and monitoring of mobile robots. This paper proposes a recurrent emotional cerebellar model articulation controller (RECMAC) neural network in meeting such demand. In particular, the proposed network integrates a recurrent loop and an emotional learning mechanism into a cerebellar model articulation controller (CMAC), which is implemented as the main component of the controller module of a vision-based mobile robot. Briefly, the controller module consists of a sliding surface, the RECMAC, and a compensator controller. The incorporation of the recurrent structure in a slide model neural network controller ensures the retaining of the previous states of the robot to improve its dynamic mapping ability. The convergence of the proposed system is guaranteed by applying the Lyapunov stability analysis theory. The proposed system was validated and evaluated by both simulation and a practical moving-target tracking task. The experimentation demonstrated that the proposed system outperforms other popular neural network-based control systems, and thus it is superior in approximating highly nonlinear dynamics in controlling vision-based mobile robots

Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel; and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are on-line tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms the proposed network is capable of producing better control performances with high computational efficiency

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Special oils for halal and safe cosmetics

Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications