913 research outputs found
Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
Current grammar-based NeuroEvolution approaches have several shortcomings. On
the one hand, they do not allow the generation of Artificial Neural Networks
(ANNs composed of more than one hidden-layer. On the other, there is no way to
evolve networks with more than one output neuron. To properly evolve ANNs with
more than one hidden-layer and multiple output nodes there is the need to know
the number of neurons available in previous layers. In this paper we introduce
Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation
that overcomes the aforementioned limitations. By enabling the creation of
dynamic rules that specify the connection possibilities of each neuron, the
methodology enables the evolution of multi-layered ANNs with more than one
output neuron. Results in different classification problems show that DSGE
evolves effective single and multi-layered ANNs, with a varying number of
output neurons
Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning
The simultaneous charging of many electric vehicles (EVs) stresses the
distribution system and may cause grid instability in severe cases. The best
way to avoid this problem is by charging coordination. The idea is that the EVs
should report data (such as state-of-charge (SoC) of the battery) to run a
mechanism to prioritize the charging requests and select the EVs that should
charge during this time slot and defer other requests to future time slots.
However, EVs may lie and send false data to receive high charging priority
illegally. In this paper, we first study this attack to evaluate the gains of
the lying EVs and how their behavior impacts the honest EVs and the performance
of charging coordination mechanism. Our evaluations indicate that lying EVs
have a greater chance to get charged comparing to honest EVs and they degrade
the performance of the charging coordination mechanism. Then, an anomaly based
detector that is using deep neural networks (DNN) is devised to identify the
lying EVs. To do that, we first create an honest dataset for charging
coordination application using real driving traces and information revealed by
EV manufacturers, and then we also propose a number of attacks to create
malicious data. We trained and evaluated two models, which are the multi-layer
perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the
GRU detector gives better results. Our evaluations indicate that our detector
can detect lying EVs with high accuracy and low false positive rate
Menjana pemodulatan lebar denyut (PWM) penyongsang tiga fasa menggunakan pemproses isyarat digital (DSP)
Baru-baru ini, penyongsang digunakan secara meluas dalam aplikasi industri.
Walaubagaimanapun, teknik Pemodulatan Lebar Denyut (PWM) diperlukan untuk
mengawal voltan keluaran dan frekuensi penyongsang. Dalam tesis ini, untuk
Pemodulatan Lebar Denyut Sinus Unipolar (SPWM) penyongsang tiga fasa adalah
dicadang menggunakan Pemproses Isyarat Digital (DSP). Satu model simulasi
menggunakan MATLAB Simulink dibangunkan untuk menentukan program
Pemodulatan Lebar Denyut Sinus Unipolar (SPWM) Program ini kemudian
dibangunkan dalam Pemproses Isyarat Digital (DSP) TMS320f28335. Hasilnya
menunjukkan bahawa voltan keluaran penyongsang tiga fasa boleh dikendalikan
Biologically inspired evolutionary temporal neural circuits
Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet
Autonomous Robots and Behavior Initiators
We use an autonomous neural controller (ANC) that handles the mechanical behavior of virtual, multi-joint robots, with many moving parts and sensors distributed through the robot’s body, satisfying basic Newtonian laws. As in living creatures, activities inside the robot include behavior initiators: self-activating networks that burn energy and function without external stimulus. Autonomy is achieved by mimicking the dynamics of biological brains, in resting situations, a default state network (DSN), specialized set of energy burning neurons, assumes control and keeps the robot in a safe condition, where other behaviors can be brought to use. Our ANC contains several kinds of neural nets trained with gradient descent to perform specialized jobs. The first group generates moving wave activities in the robot muscles, the second yields basic position/presence prediction information about sensors, the third acts as timing masters, empowering sequential tasks. We add a fourth category of self-activating networks that push behavior from the inside. Through evolutive methods, the composed network share clue information along a few connecting weights, producing self-motivated robots, capable of achieving noticeable self-level of competence. We show that this spirited robot interacts with humans and, through appropriate interfaces, learn complex behaviors that satisfy unknown, subjacent human expectative
Developing Toward Generality: Combating Catastrophic Forgetting with Developmental Compression
General intelligence is the exhibition of intelligent behavior across multiple problems in a variety of settings, however intelligence is defined and measured.
Endemic in approaches to realize such intelligence in machines is catastrophic forgetting, in which sequential learning corrupts knowledge obtained earlier in the sequence or in which tasks antagonistically compete for system resources. Methods for obviating catastrophic forgetting have either sought to identify and preserve features of the system necessary to solve one problem when learning to solve another, or enforce modularity such that minimally overlapping sub-functions contain task-specific knowledge. While successful in some domains, both approaches scale poorly because they require larger architectures as the number of training instances grows, causing different parts of the system to specialize for separate subsets of the data.
Presented here is a method called developmental compression that addresses catastrophic forgetting in the neural networks of embodied agents. It exploits the mild impacts of developmental mutations to lessen adverse changes to previously evolved capabilities and `compresses\u27 specialized neural networks into a single generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation, and does so in a way that suggests better scalability than existing approaches. This method is validated on a robot control problem and may be extended to other machine learning domains in the future
A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing
Researchers widely have introduced the Artificial Bee Colony (ABC) as an optimization algorithm to deal with classification and prediction problems. ABC has been combined with different Artificial Intelligent (AI) techniques to obtain optimum performance indicators. This work introduces a hybrid of ABC, Genetic Algorithm (GA), and Back Propagation Neural Network (BPNN) in the application of classifying, and diagnosing Diabetic Mellitus (DM). The optimized algorithm is combined with a mutation technique of Genetic Algorithm (GA) to obtain the optimum set of training weights for a BPNN. The idea is to prove that weights’ initial index in their initialized set has an impact on the performance rate. Experiments are conducted in three different cases; standard BPNN alone, BPNN trained with ABC, and BPNN trained with the mutation based ABC. The work tests all three cases of optimization on two different datasets (Primary dataset, and Secondary dataset) of diabetic mellitus (DM). The primary dataset is built by this work through collecting 31 features of 501 DM patients in local hospitals. The secondary dataset is the Pima dataset. Results show that the BPNN trained with the mutation based ABC can produce better local solutions than the standard BPNN and BPNN trained in combination with ABC
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