Presents a systematic approach for constructing reformulated radial basis function (RBF) neural networks, which was developed to facilitate their training by supervised learning algorithms based on gradient descent. This approach reduces the construction of radial basis function models to the selection of admissible generator functions. The selection of generator functions relies on the concept of the blind spot, which is introduced in the paper. The paper also introduces a new family of reformulated radial basis function neural networks, which are referred to as cosine radial basis functions. Cosine radial basis functions are constructed by linear generator functions of a special form and their use as similarity measures in radial basis function models is justified by their geometric interpretation. A set of experiments on a variety of datasets indicate that cosine radial basis functions outperform considerably conventional radial basis function neural networks with Gaussian radial basis functions. Cosine radial basis functions are also strong competitors to existing reformulated radial basis function models trained by gradient descent and feedforward neural networks with sigmoid hidden units.
Autism is the fastest growing developmental disorder in the world today. The prevalence of autism in the US has risen from 1 in 2500 in 1970 to 1 in 88 children today. People with autism present with repetitive movements and with social and communication impairments. These impairments can range from mild to profound. The estimated total lifetime societal cost of caring for one individual with autism is $3.2 million US dollars. With the rapid growth in this disorder and the great expense of caring for those with autism, it is imperative for both individuals and society that techniques be developed to model and understand autism. There is increasing evidence that those individuals diagnosed with autism present with highly diverse set of abnormalities affecting multiple systems of the body. To this date, little to no work has been done using a whole body systems biology approach to model the characteristics of this disorder. Identification and modelling of these systems might lead to new and improved treatment protocols, better diagnosis and treatment of the affected systems, which might lead to improved quality of life by themselves, and, in addition, might also help the core symptoms of autism due to the potential interconnections between the brain and nervous system with all these other systems being modeled. This paper first reviews research which shows that autism impacts many systems in the body, including the metabolic, mitochondrial, immunological, gastrointestinal and the neurological. These systems interact in complex and highly interdependent ways. Many of these disturbances have effects in most of the systems of the body. In particular, clinical evidence exists for increased oxidative stress, inflammation, and immune and mitochondrial dysfunction which can affect almost every cell in the body. Three promising research areas are discussed, hierarchical, subgroup analysis and modeling over time. This paper reviews some of the systems disturbed in autism and suggests several systems biology research areas. Autism poses a rich test bed for systems biology modeling techniques.
Predicting and mitigating human error in manned spaceflight can be the difference between mission success and lost vehicle or crewmember. The National Aeronautics and Space Administration (NASA) has used the Cognitive Reliability Error Analysis Model analysis developed by the nuclear industry during the last 30 years of manned spaceflight to predict human error. Although the analysis has proven to be reliable, it does not take into account operations specific for long duration spaceflight such as crew training and ground support. This article first explains the principles of the Cognitive Reliability Error Analysis Model and how it is used at NASA. Then, the probability for error for an International Space Station ingress procedure is calculated using standard performance shaping factors developed for the nuclear power industry. Lastly, the environmental and operational constraints of space flight are used to develop new performance shaping factors specific to a NASA‐operated spacecraft. Copyright © 2012 John Wiley & Sons, Ltd.
This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non-Euclidean algorithms which are computationally more demanding.
Human reliability analysis is a crucial for manned spaceflight success. Cognitive Reliability Error Analysis Model (CREAM) has been developed and used by the nuclear industry in predicting human error. Previously, the authors have calculated the probability error for an International Space Station ingress procedure using performance shaping factors (PSF). In this paper, the procedural risk under both ideal and common conditions using the new spaceflight specific PSFs is calculated. The risk was found to vary from the risk calculated using standard PSFs and to vary greatly depending on the spacecraft specific conditions. Under ideal conditions, the risk was found to be 1 in 88, but under common conditions, the risk was 1 in 3. Then, the new PSFs were used to analyze the impact of the three styles of training used at NASA under common conditions. Of skill‐based training, task‐based training, and knowledge‐based training, the CREAM analysis using the new PSFs showed that skill‐based training resulted in the most significant improvement in the risk of human error, from 1 in 3 to 1 in 11. Copyright © 2013 John Wiley & Sons, Ltd.
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